{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "plt.rc('font', **{'family': 'Microsoft YaHei, SimHei'})  # 设置中文字体的支持\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "# 解决保存图像是负号'-'显示为方块的问题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>GDP</th>\n",
       "      <th>人均GDP</th>\n",
       "      <th>工业增加值</th>\n",
       "      <th>第三产业增加值</th>\n",
       "      <th>固定资产投资</th>\n",
       "      <th>基本建设投资</th>\n",
       "      <th>社会消费品零售总额</th>\n",
       "      <th>海关出口总额</th>\n",
       "      <th>地方财政收入</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>山东</td>\n",
       "      <td>10550.0</td>\n",
       "      <td>11643</td>\n",
       "      <td>3502.5</td>\n",
       "      <td>3851.0</td>\n",
       "      <td>2288.7</td>\n",
       "      <td>1070.7</td>\n",
       "      <td>3181.9</td>\n",
       "      <td>211.1</td>\n",
       "      <td>610.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>河北</td>\n",
       "      <td>6076.6</td>\n",
       "      <td>9047</td>\n",
       "      <td>1406.7</td>\n",
       "      <td>2092.6</td>\n",
       "      <td>1161.6</td>\n",
       "      <td>597.1</td>\n",
       "      <td>1968.3</td>\n",
       "      <td>45.9</td>\n",
       "      <td>302.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>天津</td>\n",
       "      <td>2022.6</td>\n",
       "      <td>22068</td>\n",
       "      <td>822.8</td>\n",
       "      <td>960.0</td>\n",
       "      <td>703.7</td>\n",
       "      <td>361.9</td>\n",
       "      <td>941.4</td>\n",
       "      <td>115.7</td>\n",
       "      <td>171.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>江苏</td>\n",
       "      <td>10636.3</td>\n",
       "      <td>14397</td>\n",
       "      <td>3536.3</td>\n",
       "      <td>3967.2</td>\n",
       "      <td>2320.0</td>\n",
       "      <td>1141.3</td>\n",
       "      <td>3215.8</td>\n",
       "      <td>384.7</td>\n",
       "      <td>643.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>上海</td>\n",
       "      <td>5408.8</td>\n",
       "      <td>40627</td>\n",
       "      <td>2196.2</td>\n",
       "      <td>2755.8</td>\n",
       "      <td>1970.2</td>\n",
       "      <td>779.3</td>\n",
       "      <td>2035.2</td>\n",
       "      <td>320.5</td>\n",
       "      <td>709.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>浙江</td>\n",
       "      <td>7670.0</td>\n",
       "      <td>16570</td>\n",
       "      <td>2356.5</td>\n",
       "      <td>3065.0</td>\n",
       "      <td>2296.6</td>\n",
       "      <td>1180.6</td>\n",
       "      <td>2877.5</td>\n",
       "      <td>294.2</td>\n",
       "      <td>566.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>福建</td>\n",
       "      <td>4682.0</td>\n",
       "      <td>13510</td>\n",
       "      <td>1047.1</td>\n",
       "      <td>1859.0</td>\n",
       "      <td>964.5</td>\n",
       "      <td>397.9</td>\n",
       "      <td>1663.3</td>\n",
       "      <td>173.7</td>\n",
       "      <td>272.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>广东</td>\n",
       "      <td>11769.7</td>\n",
       "      <td>15030</td>\n",
       "      <td>4224.6</td>\n",
       "      <td>4793.6</td>\n",
       "      <td>3022.9</td>\n",
       "      <td>1275.5</td>\n",
       "      <td>5013.6</td>\n",
       "      <td>1843.7</td>\n",
       "      <td>1201.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>广西</td>\n",
       "      <td>2455.4</td>\n",
       "      <td>5062</td>\n",
       "      <td>367.0</td>\n",
       "      <td>995.7</td>\n",
       "      <td>542.2</td>\n",
       "      <td>352.7</td>\n",
       "      <td>1025.5</td>\n",
       "      <td>15.1</td>\n",
       "      <td>186.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  area      GDP  人均GDP   工业增加值  第三产业增加值  固定资产投资  基本建设投资  社会消费品零售总额  海关出口总额  \\\n",
       "0   山东  10550.0  11643  3502.5   3851.0  2288.7  1070.7     3181.9   211.1   \n",
       "1   河北   6076.6   9047  1406.7   2092.6  1161.6   597.1     1968.3    45.9   \n",
       "2   天津   2022.6  22068   822.8    960.0   703.7   361.9      941.4   115.7   \n",
       "3   江苏  10636.3  14397  3536.3   3967.2  2320.0  1141.3     3215.8   384.7   \n",
       "4   上海   5408.8  40627  2196.2   2755.8  1970.2   779.3     2035.2   320.5   \n",
       "5   浙江   7670.0  16570  2356.5   3065.0  2296.6  1180.6     2877.5   294.2   \n",
       "6   福建   4682.0  13510  1047.1   1859.0   964.5   397.9     1663.3   173.7   \n",
       "7   广东  11769.7  15030  4224.6   4793.6  3022.9  1275.5     5013.6  1843.7   \n",
       "8   广西   2455.4   5062   367.0    995.7   542.2   352.7     1025.5    15.1   \n",
       "\n",
       "   地方财政收入  \n",
       "0   610.2  \n",
       "1   302.3  \n",
       "2   171.8  \n",
       "3   643.7  \n",
       "4   709.0  \n",
       "5   566.9  \n",
       "6   272.9  \n",
       "7  1201.6  \n",
       "8   186.7  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('城市经济.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set(font='SimHei')  # 解决Seaborn中文显示问题，"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x295782780f0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1aIG5qTG92jXDytyM0HcfCHz2inEL3Gju9CvbD55iyWYP/jxwUiX5Up8HolE2vcGlUaIsaZGKhyel8bGkvU+/gkyanIw0ToJ6sTKoFywEQFrwM9kwTgHFwzhfonIRc/xfyh5PKpUS8OY9lsb6CmvvvnxHaUvZ82lmoIuuthZvImS9sRcfBXPm7nOmdbXLcSZBRvSA5JK6deuyZcsWBg4cyP379zNtCw8P5++//2bo0KEcPnw4jxJ+P06e9GaZyyzO+1yiSuUKvP8QQXBw3l/21qhOdZZu3EG9GpUJev4KE2NDrC0Vv8ADREbHcOL8ZbzWL8603vf2ParblENDQ7Xt/0b1arF0/Z/Uq1mVoGcvMTE2wtrSQnm+qBhOeF/Ea/Ny+boi1pbsPXKK/j07ci8giFdvwrLdxxflq2HDsu2HqVulHI9fhWBqpI+1heIXUYDImFhOXrrFXpeJ8nUvQ98xw20XrlOHYmyQ8wmK8mw1bVi2/SB1q5bn8cs3mBoZfDLbiUs38Vw6OT1byDtmuO7AbeoIjA0Uv8B9dT7bqizbspe61Sry+MVrTI0NsC6ouPELEBkt4cQ/vniungNAIUtzrnimX9G0bvdhilia06VlI5XkS/a/hm6fEaQE+KFRpCRpkmikH5TMP3p0F+36LUjxu4KaqTlaNe1IOnMAjdIV0W7UhtgVU1G3LIyaSQHSwl7nOFvDCkVZdvw6dcsU4nFoJKb6OlibKh4eKlLACC/fh/RrWIl7r8IJfh9NIVMDXoZHM9PzH9YMaoWxXt4Mi2fxHfQK55RogOSSpk2bcu3aNRwcHLCwsEBHR4eEhAScnJxITU1l3rx5FC9e/NM7+j8QEyOhd5/hLFk8g8TEJAYMlF3SZ1e/NlOnjKVz1wF5ksvQQJ/lM8exYvNutLU0WTxlNH73A9m05yDr5k/OUr917xF6trfH3Czzu/wb/gFUsyn3bfLNmcSKDdvR1tJi8fRx+N17yKad+1i3eEbWfHsO0rNjK8zN0t/BTxr1E7Nc3Og9bCIFTE2Y+9svKpunYqivy/IJg1ix4wjaWposGuuE36NnbDlwBrepw7LUbzt8jh72dphnmMC79+RFYuIScF7yR/o6lwkUNMvZHCFDfT2WT/iZFTsOo6WlyaKxA/B79JTN+0+zdtqILPVbD52lR8sGmbJ5nLpATFwCY5dskq/zXPpbjrPJ800dyYqtXrJ8E4bhF/CYzZ7HWDt7XNZ8+0/Qo01TzE1zaSJ5Qhxxa39H12E4pCQRv2kRGmUrodOxH3GrpmcqTb5wEg3rohgt3QmaWiQe2Ula6CvSQl+hUaoCRst2I42JJG79AkhNUfKAn89QV5tl/Zqz8q/raGtqsLBPU/xehLHF2x+3n1plqp3QoS5z9l3E0fUIZgZ6zOnZCD1tLfZeuUVMQjLjtqdflegxtjMFlfSk5IofYAhGTZoXH/QgfDPJ4U8/XZRH9Ao3zusI2YoLOvrporyi9Z2861Ii7d2LvI6QLTU1jbyOoJyuwadr8lDCojl5HSFb2p1b5nWEbOl1m/JN9pvofypH99ep1kZFSb6emAMiCIIgCEKuE0MwgiAIgpDPSKXiMlxBEARBEHLbDzAHRDRABEEQBCG/EVfBCIIgCIKQ636AHhAxCVUQBEEQhFwnekAEQRAEIb8R3wUjCIIgCEKu+wGGYEQDRBAEQRDyGzEJVRAEQRCEXPcD9ICISaiCIAiCIOQ60QMiCIIgCPmNGIIRBEEQBCHXiQaI8L35nr9xNv7NhbyOkK0ONUfldQSlYtOS8jpCtmy1CuZ1hGz5JYfndQSlHkle53WEbJU3LJLXEbLV3vteXkfI1tRu32a/P8J3wYg5IIIgCIIg5DrRAyIIgiAI+Y0YghEEQRAEIdf9AJfhigaIIAiCIOQ3ogdEEARBEIRc9wP0gIhJqIIgCIIg5DrRAyIIgiAI+Y0YghEEQRAEIdf9AEMwogEiCIIgCPnND9ADIuaACIIgCIKQ60QPiCAIgiDkNz9AD4hogAiCIAhCfiPmgAiCIAiCkOt+gB4QMQdExeLi4vI6wmcbNtSJ4Je3uXrlL0qWLPZZ99m4YSkDnByyrJ821ZlZM8erOqJSx055M2PBilx5rA792+Nxczdux9dgXcxKaZ26ujrjljjj6efBjPXT0dLWAqBkxZKsPLCcPTd2M33dNAyMDbLcd6mnC9XqV/uqfF2dOnHk9j7++Gs9hYpZZ5tvsssEjvkfYN7G2fJ8/2rVtQWr9rhkWte6mz2rPJYyb+NsipZS7beiNuzXkvnXNzLxyEIKFFX+bboaWhr85DaO5Y92MPuiK+UbVlVpjow6O3Xk4G0vNv217pPHcpLLeI747+f3jbPkx3LLyQ0cvO0lv51/eRrLwpY5zjXg597cDbzAKZ99FC+R/fOgrq7O8jVzuf/kMpu3r0L7P88zwIFj7jRoVBcAUzMTDhxz527gBY6d3kPpMiU/O1cXp44cuu3F5s84Xr+5jOeo/37mZjhe/2rZtQUr/nPuAVSpXQl37z/Q0MjZS1XNfi0Yc92NQUfnYlJM+bmmrqVB17VjmPRoKyMvrqRkw8qZthexLceQM4tRy2EelZCm5ez2HfgOjuL3LTY2li5duijd3qRJE5KSZF+V/uTJE/r3759b0XKkSpWKTJ/mTJ16bRk3biZrVi345H3mzJ5I/349sqwf/HNfpkwe8y1iKuRzyZcFK9YhlUq/+WOVrFiSfs59GdV+NGtnrueXeb8ore3g1AHrolb0rdOfx/ce0/Vn2XkzYdl4dq/Zg2PtvoSHhjN02uBM92vTuzXV7b6u8VG6YikGOTvxc9vhrJjpyvgFY5XWdnXqRKFiVnS1dSDwbiC9BneXb9M30GPEtKGsnrNOvq5q7cr0HtqTlTNcuXPVn5mrp3xVRkUKVShGmzE9cOkwhX1zttFr7s9Ka1sM7URqSioz6gznrxVeOMwfrLQ2J0pXLMVAZyeGtB3B6plujFug/Jzu4tQJ62LWdLftTeDdIHp+PJZD2o6gW81edKvZC+ee4wm8G8TbN29zlMumUnnGTxpFyyY9mD5pPguXzsy2fuDPfShWvAg1Kjbl7p0HDB05INN2x/7dadi4rnx57PhheHkcomr5xuzzPMLchZ/3PP97vAa3HcGqmW78+hnHq9vH45Xx3NMz0GPEtCG4Zjj3QNZoGb/QmbXzNpKa+vUvmAUrFKXh2K5s6zCDM7PdaT13oNLaekPbk5aSwpo6o7mwYj9tFvwk36amrkabeYPwXrAbaQ7yCOlEA+QT1NTUiIiIULpdR0cHbW1tAHx9fWndujUpKSny2/eqS+e27Ny1j5CQMK763sTCwgx9fT2l9TY25TAzM8XT62im9fr6erRp04y167Z+68hyh46fYfggx1x5rIZtGnB23zk+hH0g4FYAJgWM0dXTUVjbqG0DvDbtJyU5hSPuR2nQ2o5CJQphaGzAdZ8bAHgf9KZS7Ury+5iam9BndB/59i/VpG1DTu47TXjYe+7ffICJmQm6eroKa5u2b8zuDZ6kJKdwYPsRGrdpKN82eOIgrpzz5dmj5/J1hsaGuExewYvHLzm+9wTFShf9qoyKVGtdh+sH/iH6bQTPbwVhUMAIbSXH9fWD53jO3EJ8dBz3z93EtJC5ynJk1KhtQ07tO837zziWTdo3wuPjsTy0/QiN2jTIUtN/bF92rN6V41xtO9jj5XGYsNC33Ljuh7l59n+rHTq3Yp3rNpKTk9m2ZTdt29vLt1lYFMB5wnC8z16QrytfoQw3rvkBcOXidaw/s8em8Rccr6YZjtfB/xwvReceQLdBXfjw9gO+5699Vh5lyrepzd39F5G8jeT1rcfomxmipeRcexvwklMz/iQxOo7H525jVKiAfJvtgFbEvoviqY9/jvKoTFpazm7fAdEA+YS0tDSSk5MVrk/7+CSmpaWRmprKmTNnOHjwIB07dqRjx47Y2toSFhYGgKurK46OjvTp04cHDx4AYGNjQ//+/XFwcOD06dPyupYtW9KvXz969uzJuXPnvsnvVaRIIfzvBsiX34SEUaKE8heYgIAgnMfNkP/O/4qLi8eh9zDi4xO+SU5FVi6Yjqmxca48loW1BU8fPpMvvw97j2VRxcMwFoUK8ixAVhsXE4eJuQkA+ob6aGhqAFCyYimiPkTL7zNi9nA83DyIeKe8kZudgoUK8jjgqXw5POw91kryWRYqyJOPtbExsZh+zFe8TDF6/tSNIiUKM2ftDCpWKw/AFW9fHt0NQltXm0HjnDh//J+vyqiIaSFzXj98IV+OCougQBELhbUPfPxIiIkHoEKjqry4HaSyHBllPD4gO5ZWSo5lwUIFeZrpWJpm2m5mYYZNjYpcPH05x7kKF7Hmwb1H8uXQkLcULVZYaX2hwun1MdESzC3SX0TnLprK6uUbeRsWLl/3MCCIvgN6YmBogNMgB/4+/3mZv+TcK5jl3JMdr2JlitLjp64ULlGY2WunU+HjuWdkasTgiYPQ1ddj3qbZ1G5s+1mZFDGyLsC7h6/kyzFhkRgXVdyIfXL+Dokfz7WSjarw5vZjAHRNDGg8vgda+jp0Wz+Wko2qfHUelfkBhmDEJNRPCAsLIyYmhqSkJHlPB8h6OxYuXEhISAhdunShT58+REVFcezYMbS0ZOObvXr1Qltbm1u3bnHjxg327NmDn58fLi4u/Pnnn+jq6rJz507CwsLo3r07ZcuWBcDR0ZHBgwcTGRlJp06dsLe3V5jtcx3Yv5W6dWpmWnfipDcx0RL5cmxsHKYmJjl6nNyipqaWa4+lrqFOXEz6vJ6EuEQMFczhSK+NlS9raGgQ8iKE0FehzN40k6B7j+nyUxe2L90OgG2TWpiam3Jq72mq1vu6eQ0a6urEStIfMyEuHkMTQ8X51NWJzfC7aGjIGkV9hvUi+PlrDrofoXiZYqzyWEafxgOIfB8JwMzVU2loX58RXZUP73wpdXV1eaMCICkuET0lx/VfmtqatPvVgYPztqssx38zxUoyPtcJGJkoea6zHMvM7+Xa92nLib2nvjjD9t1rqVU783DcudP/EBOT/rcaFxePsYnyBriGhjqSDPWaHxu/TVs0xKJgAfbsPED9BnXk23dt38fxsx7UqFmVIkWt6dFp0Gdl1VBXJ+4/x8vwC4/Xv+feoY/n3kqPpfRtPJBOfduTlJDIgT8PYWhsyIItcxjReUyWXpLPoaahTqIk/VxLjk9E9xPnmoa2Jo3Hd+fcXFkPVs3+9qQkJnNz+2l0jA3ovtEZ926/Ex4Y/MV5VOY76cXICdED8gl+fn4YGBjg7e2dab2dnR1Hjx6lUKFCHD16lNDQUIYPH86QIUMIDpadlMnJyejo6HDx4kUaN24MQJUqVZg8eXKmfVlZWdGsWTOuXLmSaX10dDQ6Ooq7Cr9E9x4/U7R4zUy3D+8jMDFN/yemp6ebpXdDAEmUJFODQ1tXm7Q0xXNPJFESDDK8+Gvryhqs05xm4O97l7JVypKanMJpzzNo62ozdMZQVk1ZnaN8MVESjIzTH1NHVwepkucxJiomU+NER0eWr1ItG9Yv3Mw/Jy+yc+0egu4HUdOuurxu5vDfmfzTDOZvmp2jrBnFRUnQz3BctXS1Pzmnp/14B14/eE7A33dUliOjmKiY/xxL5c/1f4+lto52pu2tu7fk7CHv/97tkwb2/YWq5Rtnun2IiMTExEheo6unq/Q5BoiMjMY4Q72Ojg66ujrMmfcbE51nZalf4TqfAb1H0q2DEw7dBrNtl+tnZY2JisHQOPP5/qXHq1ItGzYu3MKFk5fYtdaDoPuPqWFXDZuaFdm51gPvIz4c2XkMn+P/UL95XYX7/pSEqFh0jfXly5q6WtkeP4DG43vw9v5Lnv4tG24pXKMMV9YdJeCYL367vXn013XKNK+e7T6+OTEE8+M7cuQILi4ubNmyJdsX6PHjx9OmTRsmT57M9evXCQ8PJykpCR0dHcLDwzExMeHatWv89NNPzJ07N8v9zc3NiYqKAmDPnj3079+fOXPm4OKSdWa4Kty45U/9eundmjVrVOX1m9Bv8lj5WaB/IDa2NvLlclXK8j40XGltpVqyWutiViTEJwIQGx3Lvo2yuSEea/eSmJBIuarlsCxckFWHVtUHK3cAACAASURBVOJxczdNOzVh1uaZNO3U5IvyPfR/RBXb9Jn65auW452SfA/9A6liK5t/UqiYtTxfXEwsYa/TJ0omJiQhiZJQvW5VeVf59Qs3MTY1VjrG/6Ve3n1KyVrl5MvFqpQiMvSD0nqbZjWo0b4+njO2qOTxFXnkH0hl2/T5OeWrliNcybHMWJvxWAKUKFuc5KTkHE8+/ded2/ewrZveg1mteiVCQsKyra/9sb54iSLEx8dTrUZlihQtxPEzHtwNvECX7u3YunMNXbq3w7ZOdR49kg01PH/6kkKFrdHV/fQbn4dfeLwUnXuxMXGZzr2khCQkUbHESeIIe53+OyYlJiHJ0GP7JUL9n1KkVln5snWVksSEKh/yLN2sGhU71OPkjG3pjy+JJ/rNe/lySmISCVGxiu4ufAExBJONy5cvI5VKadq0KefOncPd3Z1BgwZlqUtKSiIpKQlDQ0NSU1PZu3cvxYoVIzU1FQ0NDYyMjJBIJNStW5dt27bRunXrLPuIiIjAxsaG9+/fy4dgvqWTJ71Z5jKL8z6XqFK5Au8/RBAc/OabPmZ+dP38DYbPGobfpTuUrFCC6Mho3oUo/ifrc9gH50VjifwQRffBXbl8Kn0svVjZYpSrUpbFY5YAcP/6fbpX6SnfPnHFBE57nsH/6pdNcLvifY0xs0Zx89JtSlcoSXRENG/fvFNYe+aQN78t+ZXI95E4DOnJP6cuAeDn60+7Xq0Juv+YMjalsalRkYf+j+j5Uze6OnVinvNibBvWJDw0nAQVzfUJ8PGj24wBBF6+T+EKxYiNiCEy5L3CWosSVjgt/4UNPy8mPvrbXeZ+1fsav8waya1LtylVoRRR2RzLc4e8mfjxWPYa0oOLH48lQL0Wdbl16bbKcp078w+/L5jCxX+uUtGmHB8+RPLmtfI3Cwf3HWfpqt95H/6BYSMHcOL4Oa5dvUX5Euk9CKvXLWLv7oNcvniNSVPHMHTEAA4fOEHbDi2QxEhISEhUuv9/XfW+xuiPx6t0hVLZnntn/3O8Lnw8Xnd8/Wnbq5X83KtYowIP/R9RqJgVrbrZc/nMFcytzGnYqgFeWw584ZGTeeLjj/3Mfjy//ICC5YsSHyEhJkRxY9eshBWdVozA86dlJGY41176PqRSFzuCzt7C0NKUcq1qcX3blw+xqVQuXAX4r02bNnHixAkKFCjAkiVLsLDIOl8rLi6O3377jaioKJKTk5k9ezY2NjYK9pZO9IAo8erVK+bOncvs2bJu50mTJrFr1y7Onj0LwIsXL9i1axeRkZEMGTKE58+fM2vWLJYtW8aqVauoXbu2fK5C3bp18fb2Ji0tjfv372d5rPDwcP7++28aNWqUa79fTIyE3n2G86vzMJo0tmPAQNkldHb1a3PkkHuu5fjexUnimDd8AT2Gdqda/aosHuNCJVsb5v2ZtRfrru899m3cz6g5I4iTxLNzZfoVEAPGO7Fj1S6Sk7JOaM5pvunD59B7WE9q2FXn9zELqVK7MkvdF2apvePrz54NnoybO5q42Di2rZDNpdi+eheGxoYcvunJ8h2LWTplJTFREnat90BdQ53DtzwZNnkws0bNU1nuBEk8W0etoPmQDpSpZ4P7OFdK1SrP8K2Ts9Q2HtAGXSN9hm35jfnXNzL/+kaMC5oq2GvOxEnimDX8dxyG9aSGXTXmjVlIldqVWOKe9RL1O7538djgxdi5v3w8lul/MzXqV+P+rYAs9/lakphYhgxwZsQvg2jQqA6jhkwCoHbdmuz03JCl/urlG6x33cr8xdOQSGJZtnhttvsfP2YGnbu25dzFg/Tp153Rw7M+B4rESeKYOfz3j+deNeZ+PF4uX3C83D+eewdv7mXpjkUsn7IKSZSE4x4nCX0Vyt6ru9hyYj073Xbz8smrLPv9HEmSeA6OXEPdIe0oXt+GI87rKWJbjl7bJmaprTWgJTpG+vTcMp4x190Yc90NA0tT/D3/Jio4nFEXV/LTsXlcWXeUD09CviqPyuRwCCY6Oprg4OAst+jo6EwPc+vWLby9vdm3bx9Dhgxh9WrFw8YHDx7ExsaGHTt2MHnyZFauXPnJX0FNmhsfppDPXL58mTlz5vD7779jZ2cnX//s2TMGDRpEu3btsLW15dGjR3Tu3JnixYsD8M8//9CoUSOkUikSiQQHBwdOnZK1kpcuXYqvry8WFha8ePGCEydOYGNjQ+3atUlNTWX48OE0bdoUV1dX9PX1v7oHRFNbtR8WpUrxby58uigPdag5Kq8jKBWblpTXEbJlq6X8w52+B37JinutvgePJK/zOkK2yht+v/9TANprFsrrCNma+mLnN9lv/K7sPw/mU7Z8sMTNzS3L+tGjRzNmTPpnuri6umJmZkb//v2RSqV07tyZo0ePZrnfyZMnOX36NPPnz8fDw4O3b98yZUr2nykjhmD+IywsjDVr1rBmzRoqVqyYaVupUqXYv38/y5cvp1y5crRq1SrT9iZNZOP3S5cu5fTp0/Tu3Vu+bdKkSVkeKyAg67ukjE+8IAiCIHwLAwcOpFu3blnWG//nIw5iY2OpVEk2f0dNTU3pp303bNgQLy8v3N3dCQoKYuBA5R/49i/RAPkPKysrPDw8lG63sLBg0aJF2e5j0qRJChscgiAIgqASOfwsD2Nj4yyNDUUMDQ2Jj0+/jFkiUTwZ2N3dHUdHR1q2bElaWhpdunRR2FOSkZgDIgiCIAj5TS5dhlujRg2uXr0KwPPnzzEzM1NYJ5FI5B+yee/ePWJjP32VkOgBEQRBEIT8Jpemb9rZ2eHm5sb8+fO5efMmTk5OeHl5oaurS6dOneR1Tk5OTJo0iRo1alCgQAGmTp36yX2LBoggCIIg5De59GFiGhoauLu74+3tLb8AQ5HChQuza9eXffeRaIAIgiAIgqCUtrY2bdu2Vfl+RQNEEARBEPKb7+Tj1HNCNEAEQRAEIb/5Tr7RNidEA0QQBEEQ8hmpki/+y09EA0QQBEEQ8psfYAhGfA6IIAiCIAi5TvSACIIgCEJ+I+aACN+buKDsP/o2L33PX/YGcPz2uryOoFyqar9FV9Wk8TF5HSFb33U+Da28TpCtlJ1Zv7Dse6LRsUteR8gbYg6IIAiCIAi5TswBEQRBEARB+HKiB0QQBEEQ8psfoAdENEAEQRAEIb/JpS+j+5ZEA0QQBEEQ8hvRAyIIgiAIQq77Aa6CEZNQBUEQBEHIdaIHRBAEQRDyG/FBZIIgCIIg5LofYAhGNEAEQRAEIZ+RikmogiAIgiDkuh+gB0RMQhUEQRAEIdeJHhBBEARByG9+gEmoP2QPSHJyMmlKxsdSUlJITs75N4tev35d/vO1a9eIjo7OtP2vv/5i7dq1n7WvxMREEhIS5MsJCQlK86uC57EzNOs9gt6/TCM45K3SutB37xk+dRHN+4xk6OQFhL57D8CY2cto1nuE/FazfX9u+AfkKFOH/u3xuLkbt+NrsC5mpbROXV2dcUuc8fTzYMb66Whpy75JtGTFkqw8sJw9N3Yzfd00DIwNstx3qacL1epXy1HOz3XslDczFqzIlcfKaO/hEzTtOgCHoeMJfhOqtC70bTjDJsymWdeBDBk/k9C34dmuVxXPY2dp1mcUvUfPIDj0E+fetMU0d/yFoVMWyc+9f/ndD6Tr0N9ISU1VXbaT/9B80G/0mbCQ4DDlv3doeAQj5qyhxU+/MWz2KkLDI7LUTF/9J4fPXVZZNgDPEz40d/qVPr/OJTj0XTb5PjBi1gpaDBjPsJnLCQ3/kDXfyj84fPaiSvNlpFm7JXqTNqA7fAFqpgWV1uk4TkRv0gb5TX/WTtRLVPxmuQA8z12jxejFOM5aR/DbrMfmX2Efohjh8if2oxczfPE2wj5EfdNcXyxNmrPbd+CHbICsWrUKBwcH+c3GxibT8ubNmzPV+/j40L17dwDq1q3Lhw9ZT8qkpCQ6dOggX/b19cXb25vU1FQWLlxIYmJipvq4uLhMy3fv3uX8+fOcP3+eGzduZNr2559/Mn/+fPr06YOjoyPNmjWjXbt2ODo60qdPH969U/7P5ksFPnvJxl0H8Vy3kKm/DGLh2m1Ka13Wu2PfsDbnPdbTtH4tZi3fCIDr7xPx2bsBn70bOLx5GUWtLalascxXZypZsST9nPsyqv1o1s5czy/zflFa28GpA9ZFrehbpz+P7z2m68+yr+KesGw8u9fswbF2X8JDwxk6bXCm+7Xp3ZrqdrnT+PC55MuCFeuQ5vJHJQc+ec7G7Xvx2rKSac7DWLBqk9LaJa5bsG9SH59D22lqV4eZi9dku14l+Z69ZOPuQ3iuXcDUUQNZuHa70lqXDTuwb1Cb83vW0rReTWatSP9dUlPTmO+2jQnD+qKpoaGabM9fs8nzOHtXTGPq0D4s2uShPNsfnrSoXwPvbS40qV2V2a7umbb7+j/k6PmrKsmVni+YTR5H2btqFlOH92XRxl3K8232oIVdLbzdV9CkTjVmr878N+57J4Cj3qptHGWkZlkMrWbdSdgwlaS//kS7409KaxP3LCN+6QjZzW0C0si3pL1+8s2yBb0KZdPh83jMG8UUp44sdj+mtNZl51+0sK3EObcpNKlZgVmbD3yzXF8lLS1nt+/ADzkEM2nSpEzLNWvWxNPTU2m9qakpenp6ABgYGKCvr5+lRltbG01N2eGaMGECxsbG+Pr64ubmRrVq1XB2dmbkyJHUrl0bBwcH4uLiSEtL4++//8bGxoaIiAisrKwwNDTkwoUL7Nu3D4APHz5w5MgRDh48iEQiISkpifHjx+Ps7EyJEiUwMDDA0NBQVYcG70s36NSyMZbmBbA0L0BkVAxx8Qno6+lmqktOTuHijTssne4MQEf7RqzZtjfL/nYcPEHvTq3Q0db+6kwN2zTg7L5zfAj7wIewD5gUMEZXT4eE+MQstY3aNsBzwz5SklM44n6UBdvncfHEJQyNDbjuI2vYeR/0ZtLKifL7mJqb0Gd0H/n2b+3Q8TMMH+RI0JPnufJ4//K+eJVObZpjaWGOpYU5kVHRSp7bZC5eu8WyObK/k06tm7Fmy06l61WW7/JNOrVshKW5GZbmZrJzLyEBfV1F554/S6eNBaCjfUPW/Jn+9+tx9DQWZqY0rlNDZdnO+/rRqXl9LAuYYlnAlIhoCXEJiejr6mTJdun2A5ZOHCrL1rQerruOyLcnJiWzePNe7OvXVFk2gPNXb9OpRQP5scs23617LP1thCxfMztcdxzMnG/jbuztaqk0X0aaNnVI8fsHaUwE0pgI1PSNQUsHkrP+PWekZdee5GtnICXnPdTKeN8MoFPDmliaGWNpZkxETBxxCUno62b+/5WcksIl/yBcRvcGoEODGrh6nf1muf5f/ZA9IJ9DIpHQr18/kpOT0dHRQV1ddijU1NRQU1Pj1q1b9O/fH4A9e/bg6OjIy5cv6dOnD1KplHfv3lG8eHFKly7NtWvXKF26NBoaGujp6XHgwAF69OiBs7Mzrq6uhIWFoaWlxcCBA/nll1/Q0pING6SlpTFt2jRSUlLQ1tZm+/btrF69Gnt7e+7du8eUKVO4ffs2ampqKvu9w8I/UL50cflyQXMzQpR0s6ekphITGwtA0LNXmJkYZdqenJzCce+LdG/bPEeZLKwtePrwmXz5fdh7LIsqHoaxKFSQZwGy2riYOEzMTQDQN9RHQ1P2brhkxVJEfUgfEhsxezgebh5EvMvaVf4trFwwHVNj41x5rIzC3r6nQpmS8uWCFgUICVM8zJGSkkKMRPbcBj59gZmJcbbrVZIv/APlS/3n3FMy1KHs3IuKlrDWfT/xCYmMn7eKyzfvqibb+0jKlSgqX7YsYELIf4Z95NlSUomJlfVwBr14jZlR+nDfRs/jtLSrSbkShVWSS54vPIJyJTPmMyXk7efkC8bMOP0NzEaPo7RsYJtpX6qmZlyAtLCX8mVp9AfUTC2yv5OGBprVGpFyy/ub5QLZsEr54un/WwqaGRHyPlJhrewclA2NBwWHYmaU9Y1pnvoBhmB+yB6Qz2FoaIi6ujp///03RYoUybL94sWL2NraAuDo6IijoyNdunTBw8OD8ePH06xZM1JSUoiKimLQoEHcvn1b3uWupaVFZGQkVapUQSKRKO3BOHPmDFZWVoSEhACgqanJ3bt3CQ4OBiA4OFje66IqaWlpGOrryZf1dHWIkcRlqdPS0sS+QR3Gzl5OozrV8Tx2lvbNG2TOf9GXBrbVsrzD/lLqGurExaRnSIhLxFDBHI702lj5soaGBiEvQgh9FcrsTTMJuveYLj91YftSWfe+bZNamJqbcmrvaarWq5qjnJ9LlQ3GL5GaloaBQfo/ST1dHaIzHKt/aWlpYd/YjjHTFtConi2eh0/SvmVjpetV5cvOvdqMnbOCRrWr43n8HO2b2wHgefwcOjraOHZuRXRsHL/OW8XOVXMoV7JYjrKlpqVhqJ9+Huvp6hATG684W/0aOC9aT8NalfE6eYF2jWsDssbIhRv32L10Cpv3nchRns/Lp+TY2dXEeb4rDW2r4nXSh3ZN6n3MF8yFG/7sXj6DzV7HVZovE3V1SMxw7JITUdM1ILuXPI1K9Uh97A9J2feS5FRamhSDDP+v9HS05Y2MjLQ0NWlhWwnnVTtpVK08Xt7XaJdL88c+m5iEmr9169aNgwcPZlkvlUo5ceIEPXv2VHg/R0dHtLS00NPTo2PHjujp6dGgQQPKli0rrwkODqZYsWJIJBKMjIwU7qd169ZMmzYt07ouXbowYcIEJkyYQP369XPw2ylmbGhAdIZ/+olJyaipK37BnD9pBO2aN+Dpy9dERMXQr1u7TNuPnr1Iu2YNFN73S0iiJJkaHNq62qQpaaFLoiQYmBhmqgWY5jQDf9+7lK1SltTkFE57nkFbV5uhM4ayasrqHGfMD4yNDInJ0OBITExCXclzu2CqM+3tm/D0xSsioqLo37NztutVks/QgOgML5qJiUnKz72Jw2nXzI6nr97Izr2ubQHwf/iYwQ6daNvMDocO9rRqVJeL1++oIJt+phf0hMRkpQ3JeWMH0q5xHZ4FhxIRHUO/Ti2QSqXM37CHGSP7oqWl+vd1snzpL+oJiUnK840bTLum9XgWHCI7dp1byvKt28mMUU7fJF9G0vhY0M3QW6Cp/ckXS83qjUm5e+mb5gIwNtDLdBwTk5KV/o3MG9addvWr8fTNOyJi4ujbJuf/61RK9IDkb61atWL58uWZrkABuHr1KoULF6ZYMcXvqrS0tHBxcaF48fTu5JCQEHx8fABZN3ZAQAAlSpTgypUrGBkZERMTk2U/ampq6OhkHsN9+vSpfDjo354RVapcvjQ37z2ko30jpFIpAUHPsDIvoLBWR1sbx86tCQ4Jw6GjPVYW6XUxsXEEPn1BzcoVcpwp0D+QKnWr4H3oPADlqpTlfajirvlA/0Aq1bLhwl8XsS5mJZ8nEhsdy76N+7GpZYPH2r0kJiRSuU5lLAsXZNWhlQAYGBtQv1V9XKe58vfRf3Kc+3tTuUJZbvnfp2PrZrLnNvAJlhbmCmt1dLRx7N6B4DehOHRui1VB82zXqyRfuVLcvPeIji0ayvI9fv4Z595bHDqkn3uGBnoUsjTPUKeFkUHOu8Yrly3BrQdBdGhaT5bt6UusCpgqyaZFn/bNCA4Np1ebJliZmxHy7gOBz4MZt2gDAHEJiWioqxERI2FQ19Y5z1euJLfuB9GhWX1ZvicvsTI3U56vQwuCQ9/Rq10zWb637wl89opxC9wy5FMnIlrCoO5tc5wvo7Q3T9EoUZFUf1mDQr1wKaTR2Qx/6uihbl2CtJePVJpDkUqlinDr0XM6NKwhO47P32BppvgNoo62Fn1a1Sf4XQS9WtTBqkDuD6tmR3wSaj5nZGSEj48Pjx8/zrTezs6O2rVry5ffv3/PpUuXePnyJU5OTjg4OFC8eHE6d05/d7hjxw75z9evX6dixYqoq6sTGxv7RZNI79+/L294vHz58hPVX65Rneos3biDejUqE/T8FSbGhlhbKn+RiYyO4cT5y3itX5xpve/te1S3KYeGRs470a6fv8HwWcPwu3SHkhVKEB0ZzbsQxQ0Qn8M+OC8aS+SHKLoP7srlU+mz+YuVLUa5KmVZPGYJAPev36d7lfRerIkrJnDa8wz+V/1znPl71Li+LUvX/kHdWtV4/OwlJibGFLJSfglkZFQ0f527wL6tqz5rfU41qlOdpZt2fdm553MFr/UL5etsq1Tk+PnLNK1fi/D3kfhcvSXvHclRtlqVWbbVi7pVK/L45RtMjQywLqi4cSTLJuHEhet4rpwOQKGCBbiyJ/14rdtzlCKW5nSxV8275ka2VVm2ZS91q1Xk8YvXmBp/Rr5/fPFcPUeWz9KcK57pHwuwbvdhWb6WjVSSL6PUID+02zqh8fQe6lbFkMbFII1WPF8FQKN0FdJeBUEuXDXWqHo5lu3+i7qVSvM4OAxTI32szRU3NAEiY+I4ecWfvfOVX5knfL0frgHy559/cujQIbQzXJWhrq6Og4ODfDk+Pp42bdowbNiwTHUZGRoakpSUBMDNmzd58uQJXl5elC1blsDAQIyMjChdunSmxwDZOLerqyvDhw8nNTWVkJAQjIyMkEqlDBgwAA0NDUxNM5/waWlppKSkADB27FhatmwJwMqVsnfuqampaKjockNDA32WzxzHis270dbSZPGU0fjdD2TTnoOsmz85S/3WvUfo2d4eczOTTOtv+AdQzaacSjLFSeKYN3wBQ6cPITkpicVjXKhka4PjGEdmDpqVqfau7z32bdzPqDkjeBrwjJ0r0y9HHDDeiR2rdpGc9O1m0X/PDA30WTF3CsvXb0NbS4slM8dz+24Am3Z4sd5lVpb6P3bvp2en1pibmX7WelXkWz7DmRVbdqOtpcXiyaNk557HYdbNm5SlfqvnUXq2b4G5afq5161NM168DqXtgHEkJ6cwvF83ShfP+YRPQ309lv82nBXb96Olqcmi8T/j9/AJm71OsHbm6KzZDpyiR+tGmJvmzrtiQ309lk8dyYqtXmhpabJowjD8Ah6z2fMYa2ePy5pv/wl6tGmaa/kySYwnce9KtFv3g9QUEve5oV6sPFpNu5K40yVLuUbJSqQGB+VKNEM9XZaPcWSFx0m0NTVZNLIXfoEv2XLEB7eJA7LUbzv2Dz2a18bcRHVXIqrMdzKMkhNq0tz+sILvhIeHB9u3b1faAAHZB4Q5OzvTrl3muQ/3799nxIgRlCmT/tkXz58/x8fHhytXrrBq1So8PDyYPXs2Dx48YNWqVSxZsoTJkydTuHBh1q9fzy+/pLeomzdvTrt27bh7N+uMfqlUSqdOnejdu/dn/V5JL259Vl1e6Nho2qeL8tDx2+vyOoJyqd93o0oan3WI8XvyXefT0MrrBNlK2emW1xGypdGxR15HyJZu3V7fZL+SSd1ydH/DpVnnP+a2/9sGyLeUkJCArm7Orgz5WqIB8vVEA+Trfdcv8Hzn+UQDJEf+bxsgE7vk6P6Gyw6rKMnX++GGYL4HedX4EARBEP5P/ABDMP/Xl+EKgiAIgpA3RA+IIAiCIOQz0h+gB0Q0QARBEAQhvxENEEEQBEEQct0P8EFkYg6IIAiCIAi5TvSACIIgCEJ+I4ZgBEEQBEHIdaIBIgiCIAhCbvsRPkNUNEAEQRAEIb/5AXpAxCRUQRAEQRBynegB+dFo6eR1AqVi05LyOkL2vufvW/nOvy9E5Pt6atrf+Vc3aH/nLxOa3+//vG/qB+gB+c7PLEEQBEEQ/kt8EqogCIIgCLlPNEAEQRAEQch1+f+DUMUkVEEQBEEQcp/oAREEQRCEfEbMAREEQRAEIff9AA0QMQQjCIIgCPlNWg5vX2DTpk1069aNwYMHEx4enm3t1atXGTx48Gd9UqtogAiCIAiCoNCtW7fw9vZm3759DBkyhNWrVyutjYqKYv78+cybNw81NbVP7ls0QARBEAQhn5GmSXN0i46OJjg4OMstOjo60+NcunSJjh07oqGhQf369fHz81Oaafbs2VhZWXH69GlCQkI++TuIOSCCIAiCkN/k8DLc7du34+bmlmX96NGjGTNmjHw5NjaWSpUqAaCmpkZcXJzC/V27do2AgAC2bNlCXFwcw4YNY+fOnZiYmCjNIBoggiAIgpDP5PQqmIEDB9KtW7cs642NjTMtGxoaEh8fL1+WSCQK93f37l06duxIsWLFAChdujT37t2jYcOGSjOIBoggCIIg5Dc57AExNjbO0thQpEaNGpw8eZKOHTvy/PlzzMzMFNaVLVuWQ4cOARAfH09AQADFixfPdt+iASIIgiAIgkJ2dna4ubkxf/58bt68iZOTE15eXujq6tKpUyd5XZMmTbhw4QK9e/cmKioKBwcHeW+IMnneAElJSUFT89vFiI6OxtDQEHV1Md/2X55HTrHuz71YFTRn+ZyJFC1kpbAu9F04s13WEfj0OWVLFWfe5NFYF7QgOTmZiXOX4/8gEFMTY36fOIpqlcrnKFNXp078PH4g70LeMWP474S8ClVYp66uzqTFv9K4bUNuX7nD3DELSU5K/xbbVl1b0KF3W8Y5/iZf17qbPe17tyUmSsLGxVsIfvb6q3PuPXyCddv2YFXQghW//0bRwtYK60LfhjNriSuBT55TtnRx5k9xxtrSQun63HbslDdXb/gxf/r4XH1cz6OnWefuJTv3Zo6naCFLhXWh794ze9kGAp+9oGzJYsybNArrguaMmenC3YdB8rqoGAmbXWZSu1qlnGc74cP63YexsjBj2eSRFLUuqDhb+AfmrPmTwOfBlC1RhLnOP2FtUYDwiCjaDv4NIwN9AGwrl2fZlJE5ziXPd+ws63YewMqiAMtnjKWodTbHbuVmAp+9omyJosybMAzrguby7X73A5mzagv7NixCU0NDZfk0a7VAq0l3pNEfSDzgijTyncI6nV6/ol6kjHxZTc+QhF2LSHv5CADtmNiBWgAAIABJREFUDkNIe/WIFP8LKsvmeeYKG/afxqqAKUvHOVHUsoDCurD3kcze5EXQyxDKFrVm7ggHrMxN5dvnbPKiZoWSdGlaR2XZvpY0lz6KXUNDA3d3d7y9vWnXrh22trYK69TU1JgxY8YX7TtPX5WlUin9+vUjICAAkDVGvLy8WLduHbGxsUgkEpKScvYV7oMGDcLf318VcTO5fv26/Odr165lmTn8119/sXbt2s/aV2JiIgkJCfLlhIQE0tK+zdkV+PQFG3d44blpGVPHDmHh6s1Ka13ctmLfuB7n92+laf3azHKR/T77jp2lXKkSnN+/lV+HOTF76ef9nsqUrliKQc5O/Nx2OCtmujJ+wViltV2dOlGomBVdbR0IvBtIr8Hd5dv0DfQYMW0oq+esk6+rWrsyvYf2ZOUMV+5c9Wfm6ilfnTPwyXM2bt+L15aVTHMexoJVm5TWLnHdgn2T+vgc2k5TuzrMXLwm2/W5yeeSLwtWrPus6/RVKfDpSzbu3I/n+iVMHf0zC13/UFrrsu5P7BvV4bznJprWt2XWsvUAuM77DR+vzfh4bebwHyspam1J1Yplc57teTCbPI6yd9Uspg7vy6KNu5Rn2+xBC7taeLuvoEmdasxevQ2Ah09f0rlFQ87vWMn5HStV2vgIfPaSjbsP4bl2AVNHDWTh2u3K823YgX2D2pzfs5am9Woya0X6eZqamsZ8t21MGNZXpY0PtYJF0WrUlYQtM0g65Y52m4FKaxO9VhK/arTstmEy0sh3pL1+CoBW0x5oVlU+Z+BrBL0MYdPBs3gsHMeUQV1YvO2g0loX9yO0qF2Fc+tn0aSWDbM2esq3rfU6xbELN1WaLUdy8XNAtLW1adu2rdLGx9fK0x6Qw4cPExkZSb9+/ShTpgzVqlXj5s2b6OjosGPHDipWrMjIkSMJCgrC3d0dXV3dLPtISEhgzJgxdOzYEYBXr17x66+/oqenB0BkZCQzZ87E1NQUqVRKbGwsBw9mPgF9fHxYs2YNBw4coG7dupw8eZICBTK3kJOSkujWrRvHjx8HwNfXl5iYGJo2bcrChf9j777jY77/AI6/LntPSYxYtfcIMRJCjEqIvVJFilKqldIfqrWpPYo2iiKiQmJvRWhtNYJGSGKEEBGZlz3ufn8cF2nuYuQkop/n43GPh7vv+3v39sl9797fz/jej6xdm/+L/N8zhW/cuKG8gIupqSnNmjVTbtu4cSMPHz4kIiICiUTCvXv3MDc3x8rKCrlczsqVK7GxUX029qaCTl/Ao3M7bMtYYVvGisQkKWnpGRgZ5m/b7OxsTl8MZtG0CQB06+TCit+2AHAn8iEurRT5N2tUj5hn8UXKqW0XJw5v/4NnMXE8i4nD3NIcA0MDMtIzCsS6uLfh91+2kpOdw07fvSz2m8cWn20ADP/Wi3PHL3Dv9n1lvImZCQsnLSUy4gFPHz9l2IQhb51n0OnzeHzcHtsy1tiWsSYxKbmQtrvC4hn/A8CjcztWrNus9vHitvvAUUZ5eRJ+536xvm7Q2Yt4dHLJe+8lq3vv5XD672AW/fANAN06tGHFev8Cz+e38wADun+Mvp5ekXM7cf4qHq6tsbW2xNbakoTkFNIyMjEy0C+Q25kr/7Bo4heK3Nq1YqWf4vPk1t0H1Pqo8C7ntxV09jIeHZ2V+SUmSUnLyMDIQEXbXbrOoimKIr5bBydWbMz7Et267w/KWFrQpnljjeanU6sZOTdOI09JRJ6SiMTIBHT1ITuz0P10W3Qh+9IxyM1GUqY8EgNjcm6e12huQZf+waONA7ZW5thamZMgTSUtIwsjg/zvm+ycHM5cu83CcZ8C0NW5KSu3HQbgTlQMSSlpfNyqkUZzK4ri6gF5l0qsAAkODsbX15fAwED27dtHlSpVyMnJwc7OjuHDh/PNN9+wYMECDA0NcXR0ZNCgQa/1vBUrVmT79u3s3r0bPT093N3dAcWXvIWFBT179iywj4WFhbJgMTY2xsjIqECMnp6ecqhowoQJmJmZceHCBVatWkXDhg0ZN24co0ePplmzZvTv35+0tDRkMhl//vknderUISEhATs7O0xMTDh16hTbt28HID4+nr1797Jr1y5lj8/48eMZN24clStXxtjYGBMTk7dqY1ViYuNo3ri+8r5NGUuiY2KpVqXgB2dObg7SlDQszE0JvxeJpbkpADWqVmLvkRM0bVCHwH1HaOVQtIPSppwNV89eU95/FhNHWXs77odHFoi1LWfDnVDF2VKqNBULa8USr0rVKtL3s15cORvMjJ9/YOuvAdy6Hsa5oAsA6Bno4eU9mBMH/nrrPGOextG8ycttZ0V0zFOqVSk40SonJwdpSioW5maE3Y3E0tys0MeL07K537Pn4LFif92Y2HiaN8obKrGxtiT66TOqVbYvEJuTm/u8nUwJv/egQDtlZ+dw4PhpdqxZpJncniXQrEEt5X1bKwuin8ZRrVL5grnl5CJNTcPCzITwyCgszRTH5607DwiJuM8vW/ZgX9aGud8Mp0oF1UN0b55fPM0b1lHet7G2JDqmkLZLTcXCzJTwew+Vx21Scgo/b9pBjSoVGT97OX3dO9DaoYFG8pOYWZEbGaq8L5cmIjG3Rv7ssfqdtLTRqd+a9DVTFPs8e6zoPfEYqZGcXoiJT6J5nbwhHxtLM6KfJVDNvuDQs6Lt0rEwNSb84RMsTY0B+KiCLVM+68VUn60aza1IRAHy9urXr8+oUaMYPXo0urq6AFy7do1GjRpx9uxZ7t+/z+XLl3F2dn6r52/atClffvklH3/8MRKJhICAAH799Vfl9pSUFEaNGsXGjRvR19dXzhGRSCRIJBKuXLnC0qVL2bx5M/7+/uzdu5cHDx4wcOBAypcvT2xsLK1atSIuLo6LFy/SrFkztLW1MTQ0ZOfOnaxdu5by5cvTqlUrpk+fjrGxMUOHDqVs2bKcP6+o8GUyGVOmTCEnJwc9PT18fX15+vQpHTp04J9//sHHx4dhw4bRtm3bIrZ2HplMhomRofK+oYEB0pTUAnG6urp0cG7B1z/Mw7lFUwL2HsHdtQ0AH7dzYu3vOxgzeQ6PY2JZPmtigf3fhLaWFqkv5ZCRlo6JueqiS0tLi1RpXu+S9vNu5IEj+xF1/xG7Nu2lUrWKLN+6mIFthpAYlwjA1J++w6lDS77oqX5451VyZTKMjfOKU0MDfZKlatquTSu+mjIX5xYOBOw5jHvHNmofL26vc4XCd0Hx3svffqrfezp0cHLk62kLcXZsQsD+o7i3z98tf/TUeVo7NCzQe/K2cmUyTIzynsvQQB9pasHrHejq6tChVRPGzVmJk0MDAg+fxK1tCwDsy9owrK87datXZuPOwyxet41V08dpJL+Cx60+0hQ1+bVuxtczluLcrBEBB47j3r4VAAEHjqOvr4dn904kp6bxzezlbF4+gxoqTj7emEQLMvOWapKdicTAmMIG+bTrOJJ798Yre0mKSiaTY/zS+8RQXw9pWnqBOF0dHVyb12fcko04N6pN4LFzuLVW9BSV1DHzoSuxAkRHRwdHR0f09fO6OOfMmcPQoYqxw4CAAOWXC8Ann3xCTExMvsmkubm5tG7dmjlz5igfCwkJYcqU5xW1XE7v3r3Jzs4mOjqasWPHIpfLmTlzJk2aNEFLS4s///yTChUqFMjv9OnTyvEuT09PPD096dGjB1u3bmX8+PG0a9eOnJwckpKS8PLy4urVq8oxdV1dXRITE6lfvz4pKSlqezCOHj2KnZ2d8opxOjo63Lhxg6ioKACioqI0PkHXzNSE5Jc+9DMzs5BoqT645kz+ip0HjnPt5m0SEpMZ1KcrAIt+2cD4UUNw79CGhKRkvMb9wLZfF2Ggr6/yeV5FmpSCqVleG+kb6CNXMwdGmiTFxNxEOTyjr6/oRq3btA4+P67l9B9nAWjRrhlNWjXixP4/AZg6aibN2zgwZ810+rf+9K3yNDM1QSrN33Zaatpu7nfj2HngKMEht0hISuLTvt0Lffy/wMzEmOTUf7331Hywz5k4hp2Hgrh2M0zx3uvtnm/7vmOnGD6whwZzM0KamvellFFIbrO9h7Pr6Cmu3bpDQpKUQd07AuDt1VcZM8C9Pb/8vkeD+RmT/FJBVOhx++0odh4+ybXQCEV+PbsAcP1WBMP7e9ClnaIguR4awem/r2mkAJFnpILBSz3HOrqvHCPQaeBE9tl9RX7tVzEzNsxXcGRmZ6Ol7m/7xQB2nbzItbBIEqSpfOJW/CcIr+tDGIIp0Umo586d4+DBg2hra6OtrY1EIlH+29PTk1q18rpEt2zZwvHjxzl69KjyFhQUlK/4AMX647p167Jnzx7279/Pnj17OHjwIFevXmXPnj3s2bOHxo0VVW2vXr0KzAcBReFy6NAh+vbtW2AbKAoSXV1dDA0N6datG4aGhrRu3Zrq1fMmw0VFRVGxYkVSUlIwNTVV+TydO3dWFksv9OjRgwkTJjBhwgRatmz5eg35BurVrMa1m2GA4v8ZGn4XuzLWKmP19fTw7OWGtaU5/bt3xu75TPrroWHKrl9LczMMDfR5/ET1jPfXcev6beo71FPer9mgBrFPVP/g0a3rYdR3UHTjl6tYlox0xdlTmjSVmEdPlXGZGVmkJKXQyLEBFs9nsf996jJmFmYYvOVZc71a1bkWcgt43nZhd7BV13b6enj27koZSwv6d++ibDt1j/8XFHjvRdzDrozq1Qj6enp49uiCtaUF/T065YuTpqQRduc+TerV1lxuNapw7dadvNzuPMDOWvX1DvT1dBnY1RVrCzP6ubXDztqS7Owc9gadVcY8jU9ES1uisYm+9WpU5drN8Lz8Iu5jZ11I23XvjLWFGf27dlC2nYmxIeVsrV+K01Wu2CkqWfRdtCvkff5plauCXJqgfgd9Q7RsKyF7GKaR1y9M3Y/sufZ8OFculxN67xG2Vqqvzqmvp8vAzk5Ym5vSr0NL7NTEvQ/ksqLd3gclOgk1NjaWK1euKM/44+Li8PHxUW43NDRk/fr1b/Sc8fHxai+UAvm70jp16sSSJUvyrUABxa/5lS9fXu0aZl1dXRYuXJjvIivR0dGcPHkSUIzzh4aGUrlyZc6dO4epqSlSqVRlLvr/6jW4e/euspfnda6l/6acWzRlkc9GWjRpQPi9B5ibmRa6DDQxScqhoNMErl2ifKxCWVu27T3Cp3278U9oOA8fxxRpKem5oIt8NW0Ml89c5aNaVUhOSObpY9UFzdHdQUxc8A2JcYn0H9GXv46cASD4wnXc+nUmPCSCanU+ok7j2ty6fpu+n/Wi52APZo+bj4NTE549eaZycuvraNPSgUU//4Zj04ZE3HuAubkZ5ezUTw5OTErm4PFTbF+//LUe/9A5OzZm0WpfWjSuT/j9h6/33jtxhsDVC/M9fuHqDRrVrYm2tubOn5wdGrB43TYcG9YmIvIRFmbGlLVR/QUPkJicwqG/LhDw0wxAMfSxLuAA5WysqFW1Er9u3Uc7x8Ya67p3bt6IRWt+p0Xjes/bzoSytuqL18RkKYdOniPQ50flYw71a3PgxFlcWjblWVwiJ89fUfaOFFVuxHX0Og5C+/5NtGztkaelIE9WPzldu0o9ZI8ioBhWYjk3qs1iv3041qtOxMMnWJgYU/alpbX/lihN5fDZYLbN/+ad51YU70sRURQl2gPi5eXF8ePHmTRpEnK5HBMTE/z9/ZW3mJiYN37OixcvUq9evVcHoliNcvLkyQKra1q1apXvF//i4uKUc0AGDx7Mw4cPqVSpEt27d1feXp64+vfff1O7dm3FfIXU1DeaRBoSEsKpU6c4deoUDx48eO39XpeJsRFLZvyPTYF7uXwthPnfexP8zy3GTJ6jMn69/y76duuEtWXeAfu/MZ9xO+IeA0Z+y88btjJr4pdFGotPS0nj+1EzGDCyL41bNWLmVz9Sv1k9Fm36sUDstQvX8V8dgPessaSlprFhqWI5ou9Pv2NiZsKeywEs8ZvPosnLkCal8LvPVrS0tdhzJYCRk4Yzbczst87TxNiIpbMmsylgD5eC/2HB1PFcvRHK6ImzVMb/tmUHfT0652u7wh7/0JkYG7Fk+gQ2bd/P5es3mf/d1wSH3GbMlHkq49dv20Nf9w5YW+Y/C710/SYN69TQbG5Ghiz5bjR+u//g0j+3mTdhJMGhEXw5U3WRuH7HIfp87IK1Rd7k2FnjPmPmSl+6jpyMTCbju1GfaC4/YyOW/DCOTTsPcvnGLeZPGkNwSBhjpqqehLs+YB993V2xtshru14ft6OCnQ1dhngzYOwPDB/QnY9UTLJ9K1npZO5YgW5LN7Qr1SFztw9a9jXQH/CtynDtynXIfRShmdd+BRMjA5Z4D8bv4F9cDr3LvLGeBIfdZ+wC1cvAN+w9QZ8OLbBWMw9N0ByJvLgvBvCcTCbjypUr+Pn5kZWVxcyZMxkxYgR79+7l+PHj7NmzB11dXdq1a6dcDaOOVCplwYIF1K5dG09PT/bu3fvKL/0X1xfR09MjNDSUH3/8ET8/P1xdXTl06BD6+vrKmJMnTxISEoKHhwfVq1cnLCyMxYsXM3z4cOXzzZ49m/379yOTyfj0008ZNWoUzs7ObN68GT09PS5dusTVq1fR1tbGwsKCwMBA5b4eHh7s2rULHx8f6tSpQ8eOijHlZcuW0aJFC1q0aJFvPkyh/6/HIa8VVxLaN//q1UEl6OQVn1cHlRRt3ZLOoFDy9II9fO+T9zk/iZ5mJtK+K9mb3++eOm33/iWdQqEMmr6beV4x7doVaX+75z32JanEhmBiY2PZuHEjgwYNolUrxaSojIwMcnNzqV69OoMGDaJp06bo6urmu9xrYQ4ePMgXX3zxWj0OO3fuxNfXF72XriHQo0cPTE1N6d9f8YbOzMxk3LhxuLm50blzZ2VcdnY2oaGh+YaLXvxAz4ULF8jNzaVt27ZMnz6dmzdvsnz5cs6ePcumTZsoX758vv1e7Lt06VJu3LjBxYsX8fXNu8jQ5cuXefjwIQMGDHitNhAEQRA+fB/CEEyJ9YB8yDIyMlReNK04iB6Qtyd6QN7e+9zDAO93fqIHpGj+qz0g0c7ti7R/udMnNJTJ2yvx34L5EJVU8SEIgiD8N3wIPSDiF9oEQRAEQSh2ogdEEARBEEoZubz0X51VFCCCIAiCUMp8CEMwogARBEEQhFJGLiv9PSBiDoggCIIgCMVO9IAIgiAIQinzIVxAQxQggiAIglDKfAhDMKIAEQRBEIRSRhQggiAIgiAUuw9hCEZMQhUEQRAEodiJHpAPjCw2sqRTUMtB16akUyjU+/x7Ie/7b8FIDE1LOoXCSd7fcy15SnxJp1CqSfSNSzqFEiGGYARBEARBKHbiSqiCIAiCIBQ7cSVUQRAEQRCKnewD6AF5fwdGBUEQBEH4YIkeEEEQBEEoZcQcEEEQBEEQip1YBSMIgiAIQrETFyITBEEQBEF4C6IHRBAEQRBKGTEEIwiCIAhCsfsQluGKAkQQBEEQShmxCkYQBEEQhGL3IUxCFQXIf1DAH2dYHXgEO2tzFo33wt7WWmVcTFwi01dvJTzyMdUrlmPWGE/srC3IzslhyorNnLwcQhkLU6aPGkDLhrXeSa5Ogzri5t2PxOg41o9ZRnxUrMo4bV1thiz7ivqdHEiOTcR/0hrCztzQeD4B+4/xy+ad2JWxYskPX2Nf1lZl3JPYOKYvW0vYvYdUr2zP7AkjKWuT187BIWHMWL6O7avnoaOtrbn89v3BL5sCsbOxZsnU8diXKyS/xasJuxdJ9SoVmf2/MZS1searqQu5cStcGZckTWHtwqk0a1hXYzm+yv4jQZy/FMyc78cX22sCBOw7wi++gdjZWLFk2gTsy9mpjFO03S+E3Y2kepVKzJ745fO2m8+N0AhlXJI0hbWLpmms7QIO/YnP1n3YWVuyeNIo7O3KqM7vWQIzVvoSFhlF9UoVmPW1F2XLWOaL+X75Bhwb1KJHh9YayQ1Ap6krum17I0+OJ3PnSuSJqo9V/X7foFWhmvK+xNCEjN/nIXtwGwC9riOQPbxNzvVTGsst4Mhf+Gw7iJ21BYu/HVF42/2ymbD7j6heqTyzxg5Wtt2BPy+y+8Q5zIyN+HpQDyqXV31sCa/vg14FExsby99///1Onjs5ORmZrPRdjD/8wWPW7DjK1gUTmDysD/N/26E2duHGXbg2b8DxNbNo61CXaT7+AGzadxIdHW2Or5nFmP5uzF23/Z3kWq5WRT7+qg8Lu05m+4wN9Js1TG2s6+ce5Obk8kPzURxcGkj/OcM1nk/YvQf8umU3AT/P5bsxQ/nxZ1+1sQtX+9GhdTNO+P+MS4smTFu6RrktN1fGnFUbmDDyE40WH2F3H/Dr5h0E+Czgu7HD+HHlb+rz+2UjHZybcyJgDS4tHZi22AeAlbMncjJwLScD17Lnt2XYl7WlQe3qGsvxVU6eucDcpb8gL+bTu7C7kfzqt4OA1Qv5buzwwtvu5w10cHbkROA6Rdst+hmAlbMnc3L7Ok5uX8ee9Zptu7D7UawJOMC2pT/w3ciBzPt1i/r81m3DtWUTgjYupm3zhkxfuTHf9gvXb7HvxDmN5PWCxMYeXeeeZKz7gawjm9D7eKja2MzAZaQvH6u4rZ6EPDEW2aO7AOi69EGngZNGcwuLfMSawENsWzyZ70b0Z97abWpjF67fjmuLRgStn0/bZvWZ/rMfAFdD7+C3P4jvRvTHoW51vl+h/tgvLjK5pEi398EHXYAATJs2jfT0dJXbAgMDiY9X/BR2VlYWXl5eJCYmvtbzenl5cf36dbXbPTw8Xut5ipLD2wi6+A8eLs2wtTKnUc0qJEhTScvILBCXnZPDmeBb9OnYCoCubZpx7fZ9AGpWLs+UEX0xMzakrUNdYuKS3kmuDTs35++df5H8NIH7V8IxtjJFz1BfZeyjm/cJmLqO9OQ0Qo5fxqKc6l6dogg6exmPjs7YWlvSuG4NEpOkpGVkFIjLzs7h9KXr9HFzBaBbByeCb+b1Kmzd9wdlLC1o07yxhvO7iEcnF2zLWNG4bk0Sk6WkpavJ7+9g+rh3fJ5fG4JDbheI89t5gAHdP0ZfT0+jeRZm94GjjPLyLLbXeyHozEU8Oj9vu3q1SExKVtN22fnbrmNbgkPCCsT57dBs2524EIxH+5bYWlvQqHY1EpJTVB+32TmcuRpCn85tFPm5tCD41l3l9sysbOav8adDqyYayesFnVrNyLlxGnlKIrJHEUiMTEBX9bH6Mt0WXci+dAxys5GUKY/EwJicm+c1mtuJC9fwaNcCWysLGtX6SP1nXnYOZ4Jv0qejMwDdXBwJvn0PgJS0dKZ98Qkf2Zejp2srIh8/1WiOb0MulxTp9j4oFUMwjRo1okqVKmq3P3z4kCtXrgCKgiM8PO/D3tDQkGHD8s6cbWxsWLFiBQDlypVjxIgRLFu2jM2bN5OQkMDIkSPR1tYmJiYGFxcXpk+frnyNb775BkNDQwASExOZOnUqFhYWyOVyUlNT2bVrl/J1jIyMlP/++uuvuXdP8UZu06YNEydOVG57kxw0ISY+keZ1887KbCzNiX6WQDX7sgVic3JzkaamY2FqTPiDaCzNjBX/h6Z5Xcrnr4fRoGZljeX3Moty1oSfD1HeT4pJwKpCGZ5EPCoQe/NksPLftZwbEHk1vEBMUcU8i6d5wzrK+zbWlkTHPKNaZfsCsYq2S8XCzJTwew+xNDdV/B+SU/h50w5qVKnI+NnL6evegdYODTSTX2w8zRvl/W1srC2JflpIfimpWJibEn7vAZbmZvm2Z2fncOD4aXasWaSR3F7Xsrnfs+fgsWJ9TXjedo3rKe/bWFsRHRNLtSoVC8Tm5OYgTUl73naRyr/tC9nZ2Rw4foodaxdrLr+4BJrVzxvmtLWyIPppHNUqlS+YX04u0tQ0LMxMCI98hKWZiXLbr9v207FVUyRamv0CkphZkRsZqrwvlyYiMbdG/uyx+p20tNGp35r0NVMU+zx7rOg98Rip0dxi4hJpVr+G8r6tpTnRsfFUq1iuQGxOTi7StDQsTE0Ij3yMpenzzzyH+gBkZGbxa+AhOmm4gHsbYg5IMalYsSI7d+4EQFtbmzFjxrBq1SokEglyuZwePXooY8PCwli+fDllyxb8QgVwdXVV/tvZ2Zm6deuSk5ODsbExu3btwsXFhW3btnHs2DFcXFzy5bB9+3Z2796Nnp4e7u7uAGzcuBELCwt69uwJwMGDB/H19SU8PJwePXqwatUqHjx4wIoVK4iNjWXz5s2cPn2ali1boqOj80Y5aIJMJsPYKO/MxFBfD2lqwR4iXR0dXB0bMm7hbzg3qUPg0bO4OeU/6LKyc/AJPMz/hvbUaI4vaGlpkSHNyy0rLRPD50WQOjp6Orh9059dszXfRSqTyTAxMlTeNzTQR5qSViBOV1eHDq2b8fWMpTg3a0TAgeO4t1f0JAUcOI6+vh6e3TuRnJrGN7OXs3n5DGqo+KJ7u/zyCl9Ffqmq83Ny5OtpC3F2bELA/qO4t8/f7X301HlaOzTEyNCgyHm9CYmkZM7MVP5tU1W1ne7ztlugaLt9f+De3jlfzNFTF2jdrBFGhoYF9n9bublyTIzy/haK/FQct7o6dGjZmHE//oxT0/oEHv4Lt7bNAQiPfMSpyzfYsmgKa7cf1FhuAEi0IPOlfLIzkRgYU9h3pHYdR3Lv3oDsgr0RmpQrk2Fi+HLb6SFNVXPctmjEuHm/4tS0LoFHTuHWplm+mO9X+PLX5X/wnTvhneb8Ot6XYZSiKBVDMNra2pw5c4Z+/frRv39/zp49y8CBA+nXrx8nTpxASyvvv7Fp0yasra0LzM+Qy+VkZWVx+PBh5WMZGRlMmjSJhw8f4u3tzebNm3F0dGTq1Kk4OTlRuXLBM/umTZvi4+NDbm4uMpmMgIAAHBwclNvd3d3Ztm0btWrVYs+ePQQHB1O/fn2qVq1K3bp64oGjAAAgAElEQVR1cXZ2JjIyEu3nY/9vk0NRmBkbIU3N61rOzMpGS82H/uwxnrg5NeVuVAwJySl84t423/aftx2iVuUKODWuo3L/okpLSsHopYJD10DvlXMD3Mf359HN+4T+eU3j+ZiZGJP80gdXZmaW2jPJOd+Owq1dK+4+fExCkpRBPbsAcP1WBMP7e9ClXSv6d+1AJ2dHTv+tmVwV+eV9aWZmZqn9Qp8zcQxurk7cfRBFQmIyg3q759u+79gp3Fw1Oxb/PjMzNSb5pWItMysLiUT1x+OcSWNxa+/E3Ug1bXf0T9xcnVXu+9b5mRghTcn7gs/IUv/emz3uM9zaOHIv6gkJyVIGeXRALpczZ/Xv/DD6U3R1NX/eKc9IBYO84hcdXZAXPkdOp4ETOSGanYuiipmJUb5iLSMrW+3fdvZXQ3Br0+x526UwqKtrvu1L/vc5KyZ/wYTFa4t9ntKHqFT0gAC0bduWtm0VX4A9evQgICBAue3FkAqAnp4eq1evVhYmYWFhVK1aFR0dHQwNDdmwYQMAaWlpDB06FC8vL6RSKUOGDKFhw4bMnz+f0NBQFi5cSHR0NPPmzaNevXqEhIQwZcrzrkK5nN69e5OdnU10dDRjx45FLpczc+ZMmjRR9BIkJiYybdo0PvroI27evEn//v2VOX7//fdIJJI3zkET6laryJXQu3Rt44BcLif0XhS2VuYqY/X1dBnYxZmop3H069QaOysL5bbTV0M5ej6YrQu+1Uheqjy4cZdqzWtzac9pACrWr0rik3i18XXaNaaxe0sWdZv8TvKpV6Mql/+5TTdXJ0XbRdzHztpKZay+nh6e3TsTFf2U/l07YFdGEWdibEi5l1Yd6evpYmpspPI53ji/mtW4/E8o3Tq0eZ7fPeXrqsyvRxdFfh6d8sVJU9IIu3OfJvVqaySv0qBezWpcvhFKt45tFW0X/oq26+n2vO06Y/fS6iZpSiphdyJpUk+zq8LqVa/MlZvhdG3XQpHfnQf5jsf8+ekysGt7omKe0a+LC3bWlkTHxhF2LwrvH38BIC0jE20tCQnJKXj16lzk/GTRd9GuWIvcf84CoFWuCnJpgvod9A3Rsq2E7GHB+TOaVq9aJa6E3qGri6Oi7e4+xM66kM88NxeinjyjX+c22Fkr2vjyzQiqlLfD2sKUVo3rkCxNIz0zCyODV89zeVfel3kcRVFqChCAoUOHkpKSgr6+Pv3790cqlXLo0KECca1bt6ZJkya0aNGCwYMHs2jRogJDMkZGRsyfP59p06aRlpaGXC7n4sWLDBo0SBnj6+uLpaViCVZ6ejp169Zl3rx5KnN7UQ3v2LGDzZs3k5qaSufOnWndujUhISF4e3tTrlw5+vbtS82aNd8qB01wblyHxb57cKxfg4iH0ViYGhVYoveyRGkqh89cYdvCvELjwZNYflj1Oyu/+xwzY811M/9b6Mlgev0whLCzIZSvVZHUBCmJ0XEqY8tUtmPwki9ZPWw+6ckFu1c1wbl5Ixat+Z0WjesRfv8h5mYmlFWzhBkgMVnKoZPnCPT5UfmYQ/3aHDhxFpeWTXkWl8jJ81eUvSNFzs+xMYtW+9Kicf3n+ZlS1lb1ckOAxCQph06cIXD1wnyPX7h6g0Z1a6KtXSo6SDXC2bEJi3x8adGkPuH3XvxtX9V2pwn8Nf88j7y209zqJgBnh/osXh+IY8PaREQ+xsLUhLI2qgskgMTkFA79dZGA5VMBKGdjzbmteSdqv/jvpYJtGY0tw82NuI5ex0Fo37+Jlq098rQU5MnqTxa0q9RD9iiiWCYyODetx+KNO3BsUJOIB9FYmBpTVk1xCZAoTeHQ6UsELJmifOzvf8IIPHKKuV8P5eKN29haW5Ro8QFiCKbY6enpsWHDBgICAggICEBXV1dlXGBgICkpKcr7Y8eOxdPTk759+xIcnDdZsVq1aiQnJ7Nr1y78/f3x8PBQPndmZibm5nlVcnx8fKGFgEQiQSKR4OTkxJYtW6hQoQLOzs5oaWnRsWNHAgIC8Pf3p1evXsqJrG+agyaYGBmwZIIXfvtPcvnmHeZ9PZjg2/cYO2+NyvgNe47Tp0MrrF+aaLft8GmkaRmMW/Abrp9Pw/XzacQmaH4lTEZKOuvHLKX9iK5Ua1GHTd4rqdq0JqPWTyoQ22bIxxiYGjFy3UTm/P0rc/7+FTMb1WeIb8vE2IglP4xj086DXL5xi/mTxhAcEsaYqaonaq4P2Edfd1esLfL+hr0+bkcFOxu6DPFmwNgfGD6gOx+pmEj41vlNn8Cm7fu5fP0m87/7muCQ24yZorpoXr9tD33dO2Btmf89dun6TRrWqaFynw9V/rYLYf6UcQSH3GLMlB9Vxq/fupu+XTsWbLtrN2lYV/NtZ2JkyJJJo/Dbc5RLIWHMGz+c4NA7fDlrhcr49TsP0+fjNlhbmKncrnFZ6WTuWIFuSze0K9Uhc7cPWvY10B+guodUu3Idch9FqNymaSZGhiz53+f47Q3iUkg487w/I/jWHb6c87PK+PW7/qBPJyesLfI+8z7r2QmZTEaHEd+xcsteFoxXf0mA4iIv4u19IJGXgoGsHj16sGfPHsaNG8ejR4+Ucz50dHTYsmWLcjtAQkICnTp1wtvbm08//VRtD8gLXbt25cCBA5w6dYpt27Yxa9YsrKys6N27N1u3bkXv+TK6OXPm0KRJE7p27fpaOXt6euLvr7huxoshGz09PbZu3VpgXP51c3gdGdc0PLlMgyb2KPm184VZfPJ/JZ2Cetqqi+33hcTQ9NVBJUieUXBC6ftCnqK+p+B9kLN9bUmnUCidXp+VdAqF0q/X4Z087/nyvYu0f8vHOzWUydsrFUMwOTk5APz0008qt+fm5ir/vWDBAr755huuXLlCREQEycnJ+WJlMhm5ubnK3pP58+dz+vRpli5dysKFC/Hz8+PYsWN0796dlJQUrKysiImJISgoCG9v71fmmpGRwdWrV8nJySE7O5urV6+ydetWqlSpgpaWFqNHj6ZVq1Y4OztTrVq1185BEARBEF74EIZgSkUBEhUVlW+p7b89eqS4LkRgYCCxsbF4enri6enJwYMHCQsLo3v37soiJTU1lR07dlCzZk3Gjx/Po0ePcHBwYM2aNdjY2DBu3DiGDBmCr68vM2fO5KeffuLy5ct88cUXmJiYqM3hBV1dXQIDA/nss89ISkri999/x9PTk5YtWwIQERHB/v37AcX1Al43B0EQBEF44UOYhFoqhmDi4+NfqxdALpeTkZGRb47FCzKZTNmT8mJIIz09XWVscdJ0DmII5u2JIZi3J4Zg3p4Ygima/+oQzKmyfYu0f5sn7+YnNN5EqegBed0hCIlEovbLXEtLq8BcipIuPt6XHARBEITSRU7p7wEpVatgBEEQBEH4MJSKHhBBEARBEPLI3vvJE68mekAEQRAEoZSRISnS7U2sWbOGXr16MXz4cJ49e/bK+MWLF7Ny5cpXxokCRBAEQRBKGTmSIt2Sk5OJiooqcPv3pSuuXLlCUFAQ27dvZ8SIEa9clXn16lV+//331/o/iCEYQRAEQShlCv+pv1fz9fVl1apVBR4fO3YsX331lfL+mTNn6NatG9ra2rRs2ZIff1R9dWBQrOqcO3cun3/+eb7rc6kjChBBEARB+I8ZOnQovXr1KvC4mVn+y/enpqZSt25dAOWPqKqzaNEivLy8yMrKUl6fqzCiABEEQRCEUqaoy3DNzMwKFBuqmJiYkJ6errz/8u+svezs2bMkJSXRrVs3du58vcu8iwJEEARBEEqZog7BvK7GjRtz+PBhunXrxv3799X+KOvhw4d5+PAhgwcPJjY2lqysLGxtbRkwYIDa5xYFiCAIgiCUMsVVgLRq1YpVq1YxZ84cLl++zODBgwkMDMTAwAAPDw9l3KxZs5T/3rlzJ48ePSq0+ABRgHxwJBLtkk5BreDsVy/fKknydGlJp6Dee34pdiTv94I6iYFxSaegluzpvZJOoVDZIa8eyy9JWs1DSzqFwr2jS7EXF21tbTZt2kRQUBBubm44ODi8cp/evV/vl3pFASIIgiAIpUxxXopdT0+PLl26aPx5RQEiCIIgCKWMrPT/FIwoQARBEAShtHnTq5m+j0QBIgiCIAilzAfwUzDiUuyCIAiCIBQ/0QMiCIIgCKVMcS3DfZdEASIIgiAIpYxMIuaACIIgCIJQzD6EOSCiABEEQRCEUuZDGIIRk1AFQRAEQSh2ogdEEARBEEoZcSEyQRAEQRCKnbgQmSAIgiAIxU5MQhVKpYA/TuMTcAg7awsWjx+GvZ21yrgncQnM8PEnLPIx1SuWY9aXn1DW2pLs7By+W7mJk5f+oYyFGTO+8KRlw1oay6/74G58Nn4osdGxTB81i+iHT1TGaWlpMWG+N226OHH13DXmfDWP7Kxs1h1ejfVL/ycLa3MGtPyUp4+fFjm3gMN/4bN1v6LtJo7E3q6MyrgnzxKYscqPsMgoqlcqz6yvhlK2jGW+mO9/2ohj/Zr06NC6yHkp8zt0Ep8te7ArY8niSaOxL2ujJr94ZqzYSNj9KKpXrsCscZ9RtowVzxKS6DJ8IqbGRgA41KvJ4smjNZffviP84huInY0VS6ZNwL6cner8YuOYvvgXwu5GUr1KJWZP/JKyNtZ8NXU+N0IjlHFJ0hTWLppGs4Z1NZbjq+w/EsT5S8HM+X58sb0mQMDRc6ze8Qd2VhYs8h6Mva2VyriYuESmrwkk/EE01e3LMuuL/thZWyi3z1gTSJNaVejh0lyj+el19MCgjxey+FjSls9AFqv6uH1Bu0Y9jEZ+i3TScJDlTanU7/UpEi1tMnb4aiy3wNM3WH3oInYWJiwa1oUK1uYq47Jzcpm48TA37j/BwtiQ6Z6uNKhSlmfJqbjP8MXUUB+AptXKs2iYm8by+68qNZNQ5XJFvefq6prv8W7duuXb/m9OTk5qny8rK0u5X2xsLNHR0fTu3Zt79+7x5MkTMjMzlfF37twhKyvrtfP18PB4rbjAwEDi4+MByMrKwsvLi8TExNd+nTcVFvmYNduPsG3hRL4b1pd5vwWqjV24YSeujg0JWjuHtg71mP7LFgB89weho61N0No5jBngzpy1ARrL76PaVRk6bjAjunzBT1NX4T33K7WxPQZ7ULZiWXo7DCDsRjh9hyt+AnpEly/o1aQfvZr0Y1zf8YTdCNdI8RF2/xFrAg6wbekUvvt8IPPWbFUbu/C3AFxbNiZow0LaNmvA9JWb8m2/cP0W+06cL3JO+fOLYs3WfWxbPo3vRn3CvF9/V5/f2q24tmpK0KaltG3ekOk/bQDg1t0HdHd14oTfMk74LdNo8RF2N5Jf/XYQsHoh340dzo8rf1Of388b6ODsyInAdbi0dGDaop8BWDl7Mie3r+Pk9nXsWb8M+7K2NKhdXWM5vsrJMxeYu/QXtZ8370r4g2jW7DrG1h+9mezVg/kbdqmNXbhpL67N6nPcZxptm9Zh2q95x+fPgUfYf+qyxvPTqlgVg16DkU4ZSfrGFRh+Nq7wHSRaGA3zJn2zT77iQ8+1KwY9Bmk0t/DHz1hz5G/8Jw5gUt+2zAv8U23sjrMh1ChnzbE5wxnXvTUz/I8DcDvqGR6OtTk+dzjH5w5/L4oPmaRot/dBqSlAdu7cydy5c0lPT2fBggXKW1xcHPPnz2fz5s0q9zMyMlL5eEJCAtOmTcPNzY2TJ09y7tw5duzYQWJiIvv372fixImcP5/3BXHmzBm8vb2RyWRcvXoVJycnPD09ad++PYGBBb/EX37dr7/+Gg8PDzw8PFi4cGG+uHLlyjFixAgiIyNZtGgRCQkJjBw5Ek9PT1xdXZk5c+bbNJdaJ/6+jodLc2ytzGlUqyoJ0lTSMjILxGVn53AmOJQ+z8/Ou7VtTvDtewDUqlyB70f0x8zYCJem9YiJ01zB5NzFiSPb/yAuJo6QyzcxtzTHwNBAZWxbd2e2rg4gJzuH3b57cf64YE/Cp19/gt9P6r+I38SJC8F4tG+JrZUFjWp/REJyivq2u3qTPp2cAejm0oLg23eV2zOzspm/dhsdWjbRSF7K/M5fxcO1NbbWljSqXb3w/K78Q5/ObRX5tWtF8K07gKIAqfVRRY3m9ULQmYt4dHbBtowVjevVIjEpmbT0DBX5ZXP672D6uHdU5NexLcEhYQXi/HYcYED3j9HX03sn+aqy+8BRRnl5FtvrvRB06R882jgojtuaVZ4ftwVPiLJzcjhz7TZ9OrQAoKtzU66FRQJwJyqGpJQ0Pm7VSOP56TZzJuvUH8gT4sgNv4nE1Bz0VR+3AHqdeyJLiifn2sW8B/UN0GnoSOYR9cXV2zhx/S4ezWtja25Co6rlSEzNID0zW2Xs3SfxNKxaFoBm1SvwNDEVgFtRsdSqoLo3saTIinh7H5SaAqRPnz58//33mJiYEBwcrLyZmJgwefJkBg8eTG5uLh4eHgwcOBBPT0969+5NdHQ0ffr0wdPTk4EDB9KtWzeysrKwsrJi3rx52NnZYWdnh7+/PxcuXCA5OZnz588TFRXFokWLOHPmDABDhgyhevXq3L59m6SkJNzc3PD396dfv35oa2sr8zx48CADBgwgPDycHj168PDhQx48eMCKFSuYOnUqUVFRnD59mpycHACcnZ1Zt24dhoaGGBsbs2vXLqKjo1myZAleXl54eXlptB1j4hKpUbmC8r6tpTnRsfEqY3Nyc5GmpQMQ/uAxlmYmALRpWg9TY0MAzt+4TcOalTWWn205G+6E5n1ZP4uJw85edTe9TTkb7j6PTZWmYvFSNzOAZRlL6jSuzek/zmokN0Xb2eflamVOdGycyticnFykqWkAhEc+wtLUWLnt14ADdGzVhBqVy2skL2V+zxKoUeXl/CyIfvo6+UUp/7a37jxg487DuHzqzaBv53L/UeHd6G+UX2w8NT/Ke6/YWFsRHROrOr/cHKQpz/O7F4mluWm+7dnZ2Rw4fore7q6qdn9nls39Hgszs2J9TYCY+CRqViqnvG9jaUb0swSVsTm5uUhTnx+3D58o33sfVbBlyme90HoHV9DUsrIh90HecStLeIZWGdXHrcTYFMN+nyHRN8DIewY6DRwUGzIzSFs+HbIKFs1FEZOYQo0KeUOlNubGPI6XqoytXs6afRdvkZqRRcDpG7SspSjGb0XF4ht0hfZT1jF4SQCRT1W3fXGSF/H2Pih1c0D09PQYOHCg8v6aNWuU/9bW1mbfvn0A5OTk0LdvX8zNzdHT08Pf37/Acx08eBAXFxdq167NrFmzSE9PZ8aMGUyYMIFdu3bh6upKy5YtiY2NJTk5mX79+mFvb4+Pjw/du3dXPs/du3cJDQ3F3t4ed3d33N3d8fT0xN/fn3379lG/fn2qVq2KjY0Nzs7OREZGKoeGMjIymDRpEl988QXe3t5s2rQJR0dHpk6dypQpU6hcWXNf7gC5MhkmL/UoGBroKYuMl+nq6tDBsRHjFqzFqUkdAo+ewc3JIV9MVnY2vwQc4n9De2ksPy0tLVKff/EAZKRlYGpurD5WmherrZ2/nnYf2IVD245oLLdcmQwTo5fbTl/5Qf8yXV0dOrRszLh5Pjg1rUfg4VO4tWkGKIqRU5f+YcuiyazdfkhjuanPL61AnK6uDh1aNWHcnJU4OTQg8PBJ3Noqzpjty9owrK87datXZuPOwyxet41V01/Rnf6aZDIZJkaG/8ovVUV+unRwcuTraQtwdmxCwL4/cG/vnC/m6KkLtG7WCCNDwwL7v0uSErr8tUwmx/jl41ZfzXGro4Nr8/qMW7IR50a1CTx2DrfWjYF3nLuWFvL0l95rmRlIjExUhup17I48K5PMI7uRGJtg/M0spNO+RBZ1/52kJpPJMTbQVd431NNFmq66yOnctDq/Hb3E2NV7iU6QsmS4OwD2ZcwY1smBOhVt8T1+hcW7TrNy1OsNs78r78swSlGUmh6Ql+no6Chv6uzbt4/27dtjZGRE1apVuXDhQoGYbdu2ceTIERYtWsTjx48JDg4mJSWF69evU6tWLWrUqIG2tja3b99mz549jBo1itu3b1OhQgU6dlR0D3fq1ImcnBx27tzJs2fPlM+dmJjItGnTiIuL4+bNm/Tv359hw4axfft26tevj0QiIS0tjcGDB9OzZ0+kUilDhgzh6dOnzJ8/n3HjxrFw4UK6d+9OSEiIxtrOzNgIadpLX/BZ2Wo/mGZ/OQg356bcexRDQnIKg9xd8m3/edtBalWpgHMTzU0AlCZJMTXL++DSN9BDJlNdr0uTpJiY58Xq6efviu/cuyPHdgdpLDczE6N8X+gZmYW03ddDcWvTnHtRT0hIljLIwxW5XM6c1f78MPoTdHU1X/sr8sv7UsrIzFKfn/dw3FxacC8qmoQkKYO6K97P3l59qVtdUfQOcG/Pxeu3NJefqTHJKXkFR2ZWFhKJ6o+gOZPG4tbeibuRUSQkJjOot3u+7fuO/ombq7PKfT9EZsaG+QqOzOxstT0Zs78YgFvrxtx9FEOCNJVP3Nq88/zkqdL8BYeuPshVd/TrVK9Dxl5/ss+fIOv4PrIu/Ilu4xbvLDdTI32kaXnDVZnZOWrbbvGu03j3cGKDd1/8/zeQqX5HycjKYVx3J+pUtAWgf5sG/B0W9c7y/S8pFT0gly5dYv78+ZiYmBAbG4ufnx8GBgbI5XIePXqEl5cX6enpDB06FHd3dxITE1m/fj2///47+/fvZ8yYMYwePRp/f39MTBQHSWZmJuPHjyciIoK0tDR27dpFXFwcCQkJBAUFIZfLadSoEfb29jg7O+Ps7Myff/5JhQoVCAoK4vDhw5QtqxgrvHHjBocPH8be3p4dO3awefNmUlNT6dy5M61btyYkJARvb2/KlStH3759qVmzJqCYJzJ//nymTZtGWloacrmcixcvMmhQ3iQsX19fLC0tCzbKW6pXrRJXQu/QtU1z5HI5oXcfYmdloTJWX0+XgV3aEhUTR79OTvlm0p+6epM/zgWzbeH/NJYbwO3rYTR0bMDRXYrJXzUb1ODZk2dqY+s51OXPA39RrmJZMl46q6lcvRLZWdkamXz6Qr3qlblyM5yuLi2et92DwtvOvR1RT57R7+O22FlbEh0bT9j9KLznrQYgLSMTbS0JCdIUvHp2Lnp+NapwJSScru1aKvK78wA7a9XvHX09XQZ2dSXqSSz93Nph93x106FTF+nuqphL8zQ+ES1tCXK5XCNnz/VqVuPyjVC6dWyryC/8HnZlVK/k0NfTw7OnG1HRT+nv0Rk7m7xVTdKUVMLuRNKknuZWXr3v6n5kz5Vb9+jq3FTRdvceYWuleiWHvp4uAzs7ERUTT78OLbFTE6dJuXfD0KndgOwzxwDQrloDWbzq41aenor8WUzeA9lZyNNS3llu9SrZcuXOY7o2r6Vou4ex2Fqo7lW9cf8Jg9sreowsTQwx0NclMjaBsEfP8HCsA8DTxFS0tDR3XLyt92UeR1GUigKkWbNmBAYGsnv3brS0tFi9ejUjRoxg06ZNLFiwgOzsbLy9vTExMSEzM5OxY8cyZswYzJ6P1drb2zN48GBGjBjBL7/8gpWVFRKJhFmzZtGgQQM8PDzo27cvBgYGDB06lLVr16KlpaVyprupqSmbNm1iypQp/Pbbb1y7dg0fHx/s7RVj705OTri7uzNs2DCcnRVnaB07diQgIABbW1t69eqF4UvdxtWqVSM5OZl9+/aRnp7O9u3bGTx4MAA9evTA3FyzHx7OTeqw2HcXjg1qEvHgMRamxgWWh74sUZrKoTOXCVg0SfnYg+hYfljpx6rvvsDMWPUk37d1PugiX04bzZUzV6laqypJCck8fax6nsDx3UF8u+AbEuMS6TeiD6ePnFFua+HqyJUzVzWam3PTeixeH4hjg9p5bWej+gsUIDE5hUOn/iZg2fcAlLOx4pz/cuX2X/z3UcHWWmPLcJ0dGrB43TYcG9YmIvIRFmavkd9fFwj4aQagGJpZF3CAcjZW1KpaiV+37qOdY2ONfcg6OzZhkY8vLZrUJ/zeQ8zNTChrq3oZM0BikpRDJ04T+OvifI9fuHqDRnVr5pt79aFzblSbxX77cKxXnYiHT7AwMaasteriFxTH7eGzwWyb/02x5Jd97QKGg0eTE3IVrYpVkackI49XM7/n1nV0nTqQfeUsEgtrdB1ak3l4xzvLzalOFRbvOo1jTXsiHsdjYWxAWUtTlbEVrMwIPP0Pg9o14p/IGKKeJVGpjAWTNhymrKUptSrYsPbIRVzqVy3R4gM+jAKkVAzBXLlyBTc3N65du0ZSUhJDhw7l+vXreHp68uDBAypUqECnTp3w8/Pj008/pVWrVuzevZuBAwcSExPDwIED+eOPP3Bzc6NHjx7cu3cPPT09evbsydGjR7Gzs2PEiBEMGzaMW7du8fnnnzN8+HCVK1B8fHywtLSkQoUKBAYGMnfuXLy9vZXby5Ytm6/AAOjcuTN//fUXe/fu5dNPPy3wnLLny9AuXbrEhQsXlMtytbW1lZNVNcXEyJAlE4bht+8El27eYd7XQwi+fZcvf1ytMn797mP06dga65cmAW49cgppWgZfL1hD+xHf037E98QmJGkkv7SUNKaNmkn/kX1p3Kohs7/6kfrN6rJg09wCsdcu3GDr6kC+nvUlaalpbFiat9S1ccuGhFwJ1UhOL5gYGbJk4ij89h7j0j9hzBs/jOBbd/hy9iqV8et3HqFPZ2esLYpn0qKJkSFLvhuN3+4/uPTPbeZNGElwaARfzlyuMn79jkP0+dglX36zxn3GzJW+dB05GZlMxnejPtFcfsZGLJk+gU3b93P5egjzp4wjOOQWY6b8qDq/rbvp27Uj1pb5i/BL127SsG4NjeVVGpgYGbDEezB+B//icuhd5o31JDjsPmMXqF7KvGHvCfp0aIG1uep5GBqXnkbq8hnou/dDp04j0lbNRbtGPYwnzisQmnXiELLYJ5it8PRwB0QAACAASURBVMf0x1/J2LMF2eOH7yw1E0M9Fg9zw+9EMJfvPOLHIZ0JvhvN2NV7C8RO6NWG21GxeC7cxi8HLzDjkw4Y6usy45OOzNoahMesTeTK5Ezu66LilYqXXFK02/tAIi/uBe1vIS0tjadPn1KlShWysrLQ09OjR48e7N69m9TUVExMTHj69CnZ2dlcvHiRXr3yJkW6uLjw5595677v3LlDtWrVAFi8eDGXLl2iatWqDBw4kDp16jB06FDWrVuHsXH+LrqMjAyaNm2Kq6sry5YtIyYmhi5dutCuXTt++ukn5dlYRkYGV69eZenSpWzZsoWrV6+ydetW5HK5YtJkaiqtWrXC2dlZmceNGzdISkpiyZIlLFy4kIMHD3Ls2DG6d+9Onz59sLJSfxb7b5nXNTfpUtM6uS18dVAJOnrsh5JOQT1t3VfHlCCJ0bvv5i8KiYHqLvf3Qe6Df0o6hUJlLFpW0ikUSt+rT0mnUCjDj8e+k+f9pWLBk9k3Meah6ktXFKdSMQRjZGTEmTNnmDx5MgYGipngsbGxfPbZZ4DiS9/FxYXRo0fnKz4A0tPzzxR/8aV//PhxoqKi8Pf35+LFi/j5+XH79m1kMhn9+/dHIpGQkpLCwoULcXR05MmTJzg5OeHl5cWSJUsIDQ3l559/5tq1a/Tq1Yv69evTv39/GjRoQGBgIJ999hlJSUn8/vvveHp60rJlSwAiIiLYv38/oFhKOH78eB49eoSDgwNr1qzBxsaGcePGMWTIEHx9fZk5cyY//fTTO21fQRAEQShupaIH5F3JyclRu5ImJycHLS0ttLTyj1JdunQJUMxLeSErK4tz587RoEGDN+qtAEWB9O8hm6IQPSBvT/SAvD3RA/L2RA9I0fxXe0BWFbEHZKzoASlZhS3jVbft5cLjBT09PVxc3m5MUJPFhyAIgvDf8CH0HPynCxBBEARBKI3EhcgEQRAEQRDegugBEQRBEIRS5kO4DogoQARBEAShlBEFiCAIgiAIxU5MQhUEQRAEodiJSaiCIAiCIAhvQfSACIIgCEIpI+aACIIgCIJQ7MQcEOH98x5fcvp2yqOSTqFw7/HlziV6BiWdQqHkKfElnUKhZE/vlXQKamlXql/SKRRK/9MeJZ1CobTs65R0CiVC9gGUIGIOiCAIgiAIxU70gAiCIAhCKSPmgAiCIAiCUOxK/wCMKEAEQRAEodQRPSCCIAiCIBQ7cSEyQRAEQRCEtyB6QARBEAShlPkQluGKAkQQBEEQSpnSX36IAkQQBEEQSh0xCVUQBEEQhGL3IQzBiEmogiAIgiAUO9EDIgiCIAilTOnv/xAFiCAIgiCUOh/CHJBSNQQjl2u25pPJZMTHx5OcnFzgFh8fT25uboH4FznIZDIyMjI0mk9xCTh0kvaDv2HgN7OIehKrNu7Js3i+mLYU1yHjGTl1CU+eFfzF0++X/caeY6c1ltuQYQO4EXaKIye3U6lyhUJjtbS0WLJiFiF3zrLWdzl6egV/zXbn/k20dnYEwMLSnJ37N3Ej7BT7//Dno2pV3ji/97ntAAL2H6PdwDEMGPsDUU+eqs8vNo5RU+bT3vNLPp88jyexcfm2B4eE0fPzieT86xgocn6H/qT90G8ZOH4uUTHP1Of3LIEvpi/H1etbRk5bxpNnCQVivl++gT3Hz2out6PncP1iJp5TfiLqqfpf942JS+SLeWvpMHoWo+auISYuMd/2GWsC2fPn3xrL603tPxLED3OXFvvrBp4NocP0jXyydDuP4pLVxmXn5PLN+sN0nO5L34XbuBEZUyDmhy3H2XPxlsZyCzjyF+2HTWbg/+a/+n03ayWuwyYzcsaKfO+7A39e5PMZPzFh0VoiH6s/toqLDHmRbu+D1y5AfHx8OHnypPK+XC7H2dlZbXxubi5z5swhJSWlwDa5XI6fnx9JSUkkJydz5MgRANasWcP/2TvvsCiuLg6/u8DSkaKIgl3s2BuKFY29Yo81ajTGHjUxGjWxd2OsaKJILIi9V+xdFLAgoFgAkY50dtnd74+Ni4RdNLKK+M3Ls8/D3Htm5rd3p5w599w7K1aoTpwzZ84QEaF6fXt0dDRPnz7lypUrfPPNNzx//pwePXrw/Plznj9/TkyM9htBXiQnJ7Ny5UpWrVrFqlWr6Nixo/r/FStWkJiY88Jy9epVJk6cCMD58+f5/ffftW47LCyMPXv25FjX29tbo+3Tp0+5cOECoHJsZDKZui4rKwuFQne+bvCzcNx3HcZr1SymjxrAwo3btdou2bSL1s518dm2guYNajL79y056m/4B3LYR3c3gKrVKjF56hjaNHdjxtR5LFj6S572Q77pR6nS9tSu0oJ7/g8Z+d3gHPX9B/akabOG6uXxk7/Fe9cBnCo1Y8/uQ/y24Kf/pO9zbjuA4Kcv2LjjALvXzmf6mCEsWOuhXd8GT1yb1OfczrW0aFSHWSvc1XVyuYJ5a7bww7cD0NfT052+Z+G47z6K14qZTP+2Hws37tCub7MXrRvXwWfrMlX7/bE1R/2NgEccPndNZ9pCXkTivv8MuxZM5Keh3Vi0Zb92bdsO0bp+Dc6un0XzulWZtXG3um6t90mOXPLVma7/yvkrN5i/Yp3OH9beRcjLONxP+bJzcm9+7OnCwn2XtNruvR6IYwlrzvw6hAmdGzPH61yO+psh4Ry5HawzbcHPI3D3Po7Xsp+YPqIPCzd5abVd8tceWjeqhc9fi2hevwaz13oCcDfwCZ5HfJg+og/1qlVkxmrt55bA+/PeDkjt2rVzOBMikQhjY2Ot9suXLycgIIAdO3JfZEQiEcnJyXz99ddkZmayatUq/Pz8MDAwQF9fn5cvX7JkyRJEItVcs/fu3ePo0aNcuXKFmJgYjh49Snx8PEePHsXDw4N58+bl2P727dtxdXWlT58+6k+bNm3Yvj37hvHixQsmTJhAWFgYoaGhhIaGkpGRof4/PDycH374IUeUY9OmTTx9+pRRo0axY8cOHjx4wKhRoxg5ciTff/99Dg22trbs3LmTBw8eAHDw4EHs7XM+0fv7+zN06FCmTJnC1KlTGTp0KEuXLmXYsGG0a9eO9u3bM2zYMPU2dMG563fp0roJtjZW1KpSkYSkFNIyMnPZyWRZXLlzH7evmgPQuaUzfo+eqOszpTIWbdyBq3NdnWlr38kV710HiXoVze1bftjYWGFiov0Y69S1Lev+2IJMJmPL5h207+iqrita1JoJP4zC50z2hbBS5QrcvukHwLXLt7Arafuf9H3ObQfgc9WXLm1csLWxonY1RxJfJ5OmIUonk2Vx+XYAbh1aq/S5NsXvYYi6ftfhUxS1sqRZg9o61Xfuhh9dWjXG1saSWlUq5N1+dx/g9lUzlb4WjfB7FKquz5TKWOS+E1fnOjrT5nP7Pl2a1cPWugi1KpUlITmVtAxpbm1ZWVzxD8LNtREAnVzq4h/8HIAn4VG8TkmjnXMtnen6rxw4eppRQ/t/8v2eu/+ULvUrYVvElFpl7UhMzSA9U6bRNjQqnppliwNQv0JJol+nqusyZVks2ncZV6dyutN2w58uLRtha21Jrcrl//lttRx3fg9xa6N6sO7coiF+QU8BSElLZ9boAZR3KEH31s6fRQREmc/Pf8Hd3Z0ePXowfPhwYmM1R5CysrKYNm0agwcPxs3NjcuX3x3dfacDEhAQwF9//UVQUBCxsbE8ffo0T3ulUsnKlSsRi8Xs3r2b8PBw1qxZk+spfsyYMSxYsIBixYoxd+5cQOWYiEQiwsLCmD17NiVLlgRAX1+f27dv4+fnR3x8PDdv3iQlJYWbN29y//79XN6+oaEhI0aMYPfu3erPmDFjMDDIDtGnpqZiZ2dH/fr1KV++PDNmzMDR0ZHhw4dTvnx5WrVqhVwuV2/7wIEDXL9+nW+++YbevXtTtmxZihUrRu/evenduzczZ87MoeHbb78lKyuLxYsXM3ToUM6ePYu7uztLlixR2yQmJlKnTh1cXFzw8fHh559/pmrVqmzdupXmzZszb948tm7dipOT07t+pvcmKjYBx7IO6mVba0sio+M02mZlyUlOTQMg5Hk4VhZm6rqNuw7Tpkm9HNvKLyXt7Xh4P0i9/CoyGodSJbXalyiZbZ+clIJNUWt13W8Lp/P78o1EvxVufRQYwoDBvTA1M2XQ0D5cOPffIhCfc9up9MVTqVxp9XIxGysitYSbs+RyklNVF/6Qp2FYFTEH4HVSCmu37SU9I5PJc1dx1fee7vTFfWj7ReRsP68jtHGui+M7uuj+k7b411QqXUK9XMzKgkgN3T7wpu3SVdrCXmFlbgpAeXtbfh7WA7Go4F7SsXL+DCwtLD75fqMSU3EsaaNeLmZhysuEZI22Fe2sOXwrmNQMKbuvPqBxpVLqOvfTvrSpWZ6KJWw0rvtB2uIScxwrtlZFiIzR3MWWlSUnOe3NcfdS/ds2q1eDahVKk5EpZaP3cdrq0Pn9UBT5/CQlJREeHp7rk5SUs/vszp07+Pj4sGfPHkaMGKE18n/hwgWqVKnCtm3bWLBgAUuXLn3nd3hnEqq1tTWVK1cmPj6eJUuW0Lp1a7p06YJEIuHVq1e4ubkhlUo5fPgwr169Yvbs2VStWpUpU6YA8Ouvv7J69WoGDBjAhAkTcHZ25vjx4zx79ozhw4cDsG3bNl69ekVsbCxZWVlcuqR6aq1Xrx5GRkY8fvyYcuXKUaZMGS5fvszgwYNZuHAh33zzjVpnfHw81taqG1BGRgZXrlwhISH7AhIYGEjjxo3VyxUqVGDy5MlYWlqiVCoxMDBALBbTpEkTGjZsiEgkomfPnhgZGREWFsb69espV64ccrlc3S2iVCrJyspCKpWSlZWVo90yMjI4ePBgjrLw8PAc0ZpmzZpx9+5d9u7di6+vL+bm5syePZv27dvTtm1bzp49y5IlS9i9eze6Qq5QYGZipF42NjJUX+jfxsBAH1fnOkyY9wdN6znhfeI8HZqrnvpCnodz6XYAO5bPZJP30Q/S4bFjLXXr18xRdvbURZKTs6NsaWnpWBTRfjHV0xOT8pa9vr6qu6BF66YULWbNzr/30bhJA3X9do89HD2zi9p1nLB3sMOty9D/pPlzaTttKBQKzN6KGBkbGZKcokVfk/qMn7MCl/q12H30LB1bOQOw++hZDA0l9O/alqTUNCbNXcXfq+bgWLZUru38V+RypYb2S9esr3FtJixYS9O6NfA+cZEOzVW/Y8jzCC753mPH0p/ZtOdYvjW9QaFQYmr8ljZDCclpGrTp69O6QQ0mLN+KS60qeJ+5RocmqkiRqAAdjzcUlAaFUoGpkUS9bCzRJzk9dwQJ4KvaFfnz7B3GbjpKZEIKy4e1A+BxZByXHj5n+0Q3Np+5ozNtcoUCs7d/WyOJ9vO2US0mLNxI07rV8D55iQ7N6uewmbHag4u+9/GY/4PO9H0oynzmcXh4eLBmzZpc5WPHjmXcuHHq5StXrtC5c2f09PRo3LgxCxYs0Lg9V9fsCHR8fDy2tu+OML/TAXFwcMDBwYHvv/+e4cOHY2try+HDhwFo27Yte/fuBeDmzZv88ssvzJ8/n82bNzNkyBBEIhFKpRIbGxt++eUXli5dilwup1mzZty8eZN169YxceJEnjx5wr59+zA0NFTvt3nz5uj90//cokULbt26xapVqxg3bhxSqRSFQoFUKkUmk+Hp6cmkSZPUDkjnzp1xdnbO8T3atWtH0aJF1csSiQRbW1vGjx9PQkICTk5OxMTEsGvXLk6cOEGVKlWYMWMGAGZmZixatIgFCxbg5eWFnp4e8fHxSKVSIiMjkcvlmJiYUKpU9kX6ffpgfX19iYqKok2bNqSlpdG9e3ckEgkVKlSgfPnyODg4EBgY+M7t/BcszExyXPQzMqVaL1pzJw5n/+lL+D96QsLrZL7u2galUsm8dX8zc8wgDAw+fBDVkAHf5yqbNXcqRf55EgcwMjZCmUf+S2JiEhZFzEn750ZhaGiIkZEhc+ZOY8iAMbnsV/wxj8F9v+P2LT/Kli/Nlu1/0KZZz/fW/Lm0nXZ9piS9dWHNzJQiEmvWN2/KKPadOI9/4GOVvu7tAQh49JjhfbrQvqXq/AkIfMzlW/46cUAszExITnmr/aTa9c2dMIz9py/jHxRKQlIyX3dxVbXfhu3M/G6gztvPwtQ4h8ORKZNpjWTMHd2X/edv4h/8nITkVAZ0aKZTLYURc2NDktOzuzUyZVlo+WlZdvAKEzs706GuIwkp6Qxfe5Adk3oxf89FZvZqjoG+7vKOQMN5K5UhEmkO/s8dN5j9Z6/+c9yl8HWn1jnql08dyTW/QH5Ytolj634rUKczv5mBQ4YMoUePHrnKLf4VQUtNTaVatWqAysFNS8vtvL1NZmYmS5cu5ddff32nhneexVlZWcybN4/Lly9TokQJ5syZw6JFi3LZNWzYEC8vL8zMzNiwYYPG7bi7uyORqLzk2bNnk56erv5SvXv3zvFjxsfHIxKJCA4O5u+//+bIkSMYGxurE1ZjYmLw8PBAJpMhkUhwdHQEoHHjxuqum5SUFE6dOpWjLCIiAh8fH0xNVaG1RYsWsXHjRk6cOEHz5s05ceIEI0eOpHnz5motVlZWWFlZIZfL6dmzJyYmJvj6+pKUlESrVq1QKpXExsaSlpaGiYkJoBqh0adPn1ztUKtWdv9wgwYNEIlEuLu7U6ZMGdavX8+IESMAePz4MQ4Oug3RA1R3LMudByF0atkYpVJJ4JMXFLex0mhrKDGgX6fWhL+KoXeHlhS3sSIyOo7gp2FMnK/ynNMyMtETi0lISmFoz/b50uZ/9z6NnOuz1/sIADVrVSMyMneG/Nv29RvW4cjBk5QuY096ejo1a1fH3qEER0/vAsDcwpx2HVvx4+RfqdegFkFBjwF4FvqCEiXtMDIyJENDf7AmPue2U+krh+/9IDq3bqrS9/gZxW2sNdoaSiT07/oV4ZHR9OnkSvF/uq/MTI0pYWvzlp0B5qYm+dYGUL1iGe48DKFTy0bZ7WdtqUWfAf06tSI8Kpbe7Vuo2i8mjuCn4UxcsA54034iVfv1+Cpf2qqVd+DOo6d0cqmr0vY0AlvrItq1fdWU8Kh4ers2prgWu/8nqpey5U5oJJ3qVVK1X0QstkXMNNreex7FoBaq66CVmTFGEn3uv4gm+GUck7acACAtU4aeWExiagZDWuUvF6l6hdLcCXxCpxYNVdpCwyhuk8dv26EF4a9i6f1VM4rbqI5P34ePKVuyODaW5jjXrkpSchrpmVJMjAw1bqcwYGFhkcvZ0ISZmZn6Xg1oHFjyNr/88gs9e/Z8r9SBdzogR44cwcTEhPbt2+Pm5sadO3dQKpUaPb81a9YQEhKCvr4+4eHhFC9eXJ13IZPJqFSpkjpXIioqismTJ6sTQ729vXNFQACKFClC3bp1USgU9O3bFycnJ/766y/Wr19PYmIiM2fOZOvWrer1ypYty65dqpvP4MGDc5UNGjQIPT09Xrx4wZYtW7hw4QI9e/Zk3bp1bN++nfnz57Nu3ToWL15MzZo1+e677yhdWtWv7uzsjIODAxKJhE6dOqn3KZfLSU1NVbfJgwcP2LRpE9evX6dcuXKcOXOG0aNHI5fLOXXqlHq9w4cPM3/+fEqWLEl6ejoWFhbqRNXw8HDKlCmj82x2l3pOLNvsRcOaVXj8PAJLC1Psimm+SQEkJqVw/OINdv8+B4AStjZc271WXb9ux0HsbW3o1kb7iKj35ezpi/w6/ycuX7xOlaqOxMcn8jLilVb7/XuOsnTVr8TFxvPtd4M5fvQsN6/foVKZ7JEvv69biNeO/Vy9fJOp08cxcvRgDu47TvtOrUlJTnlv5wM+77YDcGlQi6Xu22lUuzohz8IoYmGGna32vvTEpGSOn7+G9/rskGq9GlU4eu4qLRrXJTYukfPX76ijI/nWV68Gy/7y/qf9XmJpbvYe7XeT3atUo6FKFLPh2q7V6vp1Ow9hb1uUbq5N8q+tVhWWeR6mYfWKPA57haWZKXY2mp0jgMTkVE5c9cNr0aR87/tLoGnV0iw7eJWGjvY8jozH0sQIOyvNDoi9tQXeVx/wdfOa3H8RTXhsEtVLFePKwhFqm/UnblHS2pxuDavkW5tL3eos27qXhk6VePwiEktzU+yK5nHcJadw/PJtdi//WV12634w3icvMX/8EG7eC8LWxrLAnY9PNZS2du3anDhxgs6dO/Ps2TOsrDQ/dAEsXLgQGxsbBg4c+F7bfqcD0r17dwB++kk1ZPHrr7/WavvGuQgJCWHy5MlMmzZN3V1Sv37OvrSjR4/SrFl26HLgwIG5IiAAxYsXp3v37ty6pRpXf+/ePby8vOjevTtKpTLHkFXI2Qf65v9/O0sikQhra2saNmxI1apVOXDgADdu3ACgX79+NGnSBE9PT27cuEGJEtmJaQcPHuTOndx9kyEhIezZs0c9Kmjp0qUsWbKE9evXs2/fPlavXk1QUBCVK1fm2rVrPHr0iEmTJtG5c2eio6NxdHSkefPm9O3bl6JFi+Lk5MSlS5dISUmhVatWWtv7QzAzMWb59O9Y8Zc3Bgb6LPzhW/wCH7Np9xHWzp6Yy/6vvcdxa9cCG8uPn9iWkpzKiMETmDV3KlKplDEjpgJQv2EdJk4ZxcA+o3PYX796m/V//MW8RT/z8EEQyxat1bRZNZPHzWTJijmMn/wtYS8iGDvqx/+k73NuOwAzUxOWz5zAis07kBgYsOjHMfg9CMZ910HWzZ2aW9/uw/Tq2Boby+ynwR7tWvI84hXtB09EJsti1Nc9KF9aeyLwf9JnYszyH0exYuseDAwMWDh5OH6BT9jkfZS1s8bn1rfvBG7tmn2S9jMzMWL5xEGs2HEUib4+C8f2xy/4GZv3n2XNj8Nz2W85dA4310bYaHnK/3/DzEjCsqFfsfLwNST6eiwY6Irf01dsPuPLmpGdctj+0K0Jc3ado/+KPViZGTOnXyuMDXPP4aMzbSbGLJ86khUe+1Xn7cRh+D16wqY9J1g7M3dX8F/7T+HWtik2ltndwcO6t+WXP7bhOmI6JYtZs3jyN7nW+9R8qoHWzs7OrFmzhnnz5uHr68ugQYPw9vbGyMiILl26qO2uXLnCtm3bqFWrFv3790cikeDhkfdwZZHyPR+xf/rpJ4YMGcLevXsJCAhAT0+PkJAQHB0dUSgUtGrVitGjR3PmzBl27tzJ/PnzsbOzIyIignHjxjFlyhSaNFE9qWRmZtKuXTs8PDwoU6YMHTt2ZP/+/TkiIC4uLpw/fx59fZWPdO3aNWJjY/njjz+YO3cu169f59ixY3Tv3p0hQ4aouz4aNWqEnZ0doJrnw8fHJ0fZixcvuHbtGkZG2UlJb7p5KlasyI0bN/j999/V+32bLl26qPNf3mbo0KHMnTuXUqVKERUVxaRJk9ixYwf9+/dn586dyOVy9PT02L59O05OTnh6ejJ9+nSsra35888/OXDgAJaWlshkMv7++2+mTZtGx44d1fOu/Prrr+p8mHeRGazbia10SekGI95tVIC8uLW5oCVoRSQxerdRAaLMSH23UQGiTHtd0BK0ole6RkFLyJOsG4cKWkKeiMvqbpTgx8Cwuuu7jT6AUWV752v9jc80z0ulCalUio+PD8WKFaNevXr52u/bvHcml1QqRSqV5hpu+jZvIh/lypVj6tSpGBgYYGpqSqVKlZgzZw6rV6+mSpUqPHjwgAoVKlCmTBmAXFGMqVOnUqZMGbUT8M033/D06VPq1KnDihUrqFGjBo0aNaJnz56sX7+eDRs2MHnyZADs7e3Zt28fkB2tebvM3d09V0SkYsWKdOjQgcOHDxMaGsqNGzdo2rRpru8XFxdH//65x9iHhIQglWZnfL/JINbX16dv377qvJe4uDg8PT1zDE+SyWRMnTqV5s2b06NHD0aNGoWrqytt2rShdevWTJs2jcTERGxsdDcsTUBAQECgcPMpp2KXSCS0b6+brti3ee8IyPuiUCgQi3NnGEdERFCiRAmNdf9GJpPlmLPj38sfk6SkJMRiMWZmuUOrUqlU7Ux8LDIzM3NEgv7z+kIE5IMRIiAfjhAB+XCECEj++H+NgIzMZwRk03+IgHwsdD4WUJuD8e9ZQPPi387Gp3I+IPcQpLf52M4HkC/nQ0BAQEDg/4P8zgPyOSC8DVdAQEBAQKCQ8SW8DVdwQAQEBAQEBAoZX0IE5L1fRicgICAgICAgoCuECIiAgICAgEAhQ+iCERAQEBAQEPjkKHQ8S3ZBIDggAgICAgIChYzC734IDoiAgICAgECh41O9C+ZjIiShCggICAgICHxyhAiIgICAgIBAIeNLGIYrOCBfGBkL5xS0BK1UMnv/2XALgqy/1xS0BO1IhFM1P8geRBS0BK0YDuxW0BLyRL9R14KWkCfywCsFLaFAEEbBCAgICAgICHxyvoQcEMEBERAQEBAQKGR8CV0wQhKqgICAgICAwCdHiIAICAgICAgUMoQcEAEBAQEBAYFPjlKYCVVAQEBAQEDgU/MlJKEKOSACAgICAgICnxwhAiIgICAgIFDIEHJABAQEBAQEBD45X8IwXMEBERAQEBAQKGR8CTkgggMiICAgICBQyPgSRsEISagCAgICAgICnxwhAiIgICAgIFDIEJJQBQolkpadMew+GEV8DGnr5qKMfZWnvV7FahgPnUzKrG9BoTrs9WvUR9K2JyjkZB76G/nToHxp6jaoM8MmDyEmMoZZo34jMkyzJrFYzJRFE2nWvil3r/kzd9xCZFKZur5N99Z07Nueyf2n5VivRv1qTFvyA8PajkQu182pq1+/DQateqFMiiPTaxXKxBiNdob9pyB2qKheFhmbkeExD8XzRzrRkUNT3dYYNO+JMimezH1/aNfUexJi+wo5NW1fiOKF6neUdBqBIiyIrIBL/zf6JG26YOQ2VHVerJqDIuYd54VjdUy+nULyj8PV5wWAYY+B2awjGgAAIABJREFUiMR6ZOz10Jk2AO+rD9hw8hbFi5ixdMhX2NtYaLSTZcmZtu00955HYWlqxOy+LXEqUzyHzcwdZ2lQ0Z5uDavoVOO7OHLSh+u3/Zg3Y/In3e/u875sOHSR4lYWLB3dE4diVhrtZFlypm7cx73QCCzNjJk9pDM1y9trLS9IvoQk1ELRBaNUKtmwYQPx8fEApKSkMHjw4DztR4wYQXR0NAABAQF8++23ee4jMzOTjh076kxzWFgYe/bsUS9fvXoVb29vjbZPnz7lwoULACgUCmSy7BtqVlYWCoXufF2xQzkMuw4kZc53ZGxfg/HAcXmvIBJjPGg8GV4b1RdZUbESGA0YQ8a+LUgvn8Rk7Jx8aSpfpRxDJgxiePvRrPplDZPma9fUbVAX7ErZ0aNeX4LvhdB7eE91nbGpMaN/HsEfc9blWEcsFjN5wQTWzt2oM+dDZFsKg5Y9ydgwHemxrUg6D9Nqm7lzGelLR6s+a35AmRiNIuKJTnTk0FTMAQOX7mRsnon05DYk7YZo1+S9kvRVY1WfDT+iTIxBEREKgEELN/Sdmv5f6ROXKodRj0Ek//wt6VtXYzxsQt4riMSYfDOR9L/X53A+JK07YdTta51qAwh5GYf7KV92Tu7Njz1dWLhPu+O193ogjiWsOfPrECZ0bswcr3M56m+GhHPkdrDONb6L81duMH/Fuk+euxASHo37kcvsmjWCnwa0Y9GOk1pt9168i6N9Mc4un8hEN1fmbD2SZ3lBokCZr8/nQKGIgIhEIkqXLk3fvn1ZvXo1fn5+hIeHM3ToUEDlcKSmprJhwwaKFi2KSCSibdu2bN26lWnTprFv3z46deqUa7sXL14kLi6OHj16YGhoiIGBgbpu06ZNtGnThnLlygEqJ2bevHlaNU6fPp06deqol21tbdm5cydVq1alevXqHDx4kG7duuVYx9/fn5UrV5KcnExYWBjVqlWjatWq3Lt3j5iYGEQiEcWKFWPatGk4OTnlpwnVGNRpgvTqaZSJccgT4xCZWYDECKQZGu0lrl1RvE4g694tdZnYwpKMv9egeB6C4nkIDJkERsaQkf5Bmpq1b8rJPaeIi4ojLiqOIlZFMDI2IiM9t6YWHV3Ysc6LLFkW+z0OsdRzATvWewEwfMpQrp29wdOgZznW6TG0G/HR8dw4d/OD9GlCv2oDsvwuokxOQJmcgMjEAgwMQZaZ53oGzh2R3TwNWbI87T5IU+X6ZN27jDIlEWVKIiITs/fT1Kg9sttnQC5DVLQkIiNTsh5e/7/SZ1DfBemlUygT4pAnxCEyLwKGRpCp5bz4qjuK1/Fk+b91TBkaoV+zIZkn9+tUG8C5+0/pUr8StkVMsS1iSmJqBumZMowNDXLZhkbF07xaGQDqVyhJ9OtUdV2mLItF+y7j6lRO5xrfxYGjpxk1tD8hT5590v363A2ii7MTtpbm2Fqak5CSRlqmFBNDSS7b0MgYmtd0BKBe5dJEJybnWV6QfAlJqIXCAQHo2LEjNWrUwMrKijlz5nDkyBFMTExy2e3evZtdu3YhFquCO3369CEoKIj79+/j4eFBz549GThwIADJycnExGgOAT9+/BgXFxf1clpaGuXLl2fRokW5bGfMmEF6es6b77fffktWVhaLFy9GLBZz//59oqKiuHz5MtOmqboHEhMTqVOnDgqFgpEjR/Ly5UsePXrEDz/8wOLFi2nXrh116tRBT0/vwxpNAyLrYsgf+auXlYlxiIvaonj5IretqTlGPYYiD3+KyZhZSM8fIevhHeRPAlUGenpIWnZB/jzkg50PgGIlinH3qp96OTYqDjuH4jwLea7R9kmg6kk4NTkVSxtLAEpVcMBtWHfuXPVj9toZ7NroTVBAMOaW5gyfMpTQR0+Z6z6bg55HuH3J94O1vkFkYY382UP1sjIpHpFlUZQxEdpX0tNDv6YL6eumabfJr6bngdmakhMRFbFBGftS+0piPfRrNCHd/WfVOrEvVdGJLnlHDL80fWLrYmQFZh+DioRYxEWLo4jIfQyKTM0x7j0MeVgoJhPnID17mKx7vpCZQdqq2Ri5aY/sfChRianUr1hSvVzMwpSXCclUsLPOZVvRzprDt4KpU64Ee649pHGlUuo699O+tKlZHpFIpHON72Ll/BkcPHbmk+83KiGJBpXLqJeLWZoTGfeaCiWL5bKtWNKWQ1cDqONYij0X7uBcrVye5QL5o1A4ID4+PoSHhzNw4EBWrFiBRCKhb9++SCQSJBIJcXFxTJ06lbZt29KjRw969uyJvr7qq3Xr1g1/f9UNV6lUIpfL32uf/z5B32xPG/92EjIyMjh48GCOsvDw8BxRlGbNmnH37l327t2Lr68v5ubmzJ49m/bt29O2bVvOnj3LkiVL2L1793tpfh9EYjHK9DT1slKaoXoS1YCkZWeU0kykZw8iMjHFZOwcUuaPRxHxDACj3iORuHYjbc2cfGnSE4tJS8nWlJGWgVkRU422YrGY1ORsWz09laPZ79vehD+L4MC2Q5SuUIqVu5YyoNkQugzoiDQjk31bD2BmYcb8zXMY3XVcrijJf0Yshsy3nC5ZJiIj0zwDm3rVGiF/HADSvJ/4PxjRB2iq2hB56L13RiF0wues71/nBZl5nBdtuqKUZpJ58gAiUzNMJ/1G8qzvUYQ/+2jyFEoFpkbZT+zGEn2S06Uabb+qXZE/z95h7KajRCaksHxYOwAeR8Zx6eFztk90Y/OZOx9NqzYKwukBUCiUmBobqpeNJQYkp2mObH3VoCqbj13m+993ERn3mhVjeuVZXpB8Lt0o+aFQOCCNGjXC19eXmTNnMmfOHLZs2UKDBg04d+4cnTp1wtfXFyMjI4Ac3Sj/RiQS5XAkTE1NOXPmDGfOqLzy58+f06dPH3W9RJI7RPe+vE94zNfXl6ioKNq0aUNaWhrdu3dHIpFQoUIFypcvj4ODA4GBge/czn/SlZqCyCT75i4yMASFZq16FaqSeWwXspvnVcvlq6Lv1BDpPw5Ixq4NSC+fxHTqEpJ/HPzBUZDk18mYWWRf7CVGEhRaNCW/TsasiJm6e0byTxi1Wt2qbFywmcunrgLQsGUDajvXpGqdKvy9dhc+h1TfoXrdqjRu1TDfDogyPRWM3orA6UtAmXd+iX6tZsguHczTJl+aMv6tyeDdmpyaIrt6+KNpepvPWZ8yNTmnw2FgqFWbfsWqZBzaiey6KrdCr2JVDGo3IvMjOiDmxoYkp2c7YZmyLMRa7ufLDl5hYmdnOtR1JCElneFrD7JjUi/m77nIzF7NMdDXXUS1MGBhapTD4VC1nebGW+Z1mkm9XOnQqAYJyWl8s2QbO38ZrrXcSKL9fvOx+RKSUAuFA2JqasrUqVNJSUlh165d7NixgxMnTqCnp8etW7eIiYnByMgIc3NzlixZgrGxsXrdZ8+e0b9/f0DlFKSnp+Pp6YmFhQUnTpxgx44dOaIlb6INgYGBXL16lQoVKuQW9B6IxeIczswbatWqpf6/QYMGiEQi3N3dKVOmDOvXr2fEiBGAqgvIwcHhg/adF/JnwehVqoHsug8AemUqokiM1WirTE9FERedvSyToUxLQVyqAmSkoYiJRBH+VNWNY22L4mXucPX78CggmJoNnTi9/ywAlZwciX2lWVNQQDA16lXj/NGLlChlR8Y/F+XU5DSiIrK1SjOkpLxOJS0ljaiIqOzyTCkpSSkfpPNtFC9D0StTBXnAFQDEJcuhTErQvoKhMWK7MupRHB8DRWQoeqUqI7+vcsLEJcqiTH6HJtvSKMI+TULi56xPHhqMfhUnZFdUDyN65RxRxGs/L5Sx2ccUMinKtPwfU3lRvZQtd0Ij6VSvEkqlksCIWGyLaI7Q3HsexaAWquuMlZkxRhJ97r+IJvhlHJO2nAAgLVOGnlhMYmoGQ1rV/qjaC5pqZUpwJySMTo2dVG33IhJbK3ONtgGhEQz6qjEAVuYmGBsa8DL2tdby8iWLfrLv8W8UQg7Ip+P169d8//33bN26leDgYCZOnIiXlxejR49m/fr1tGzZkmrVqrFjxw71OgcPHiQgIIAePXpodAYCAwO1dq2kpqby9OnTD9L64MEDNm3axPXr1ylXrhxnzpxh9OjRyOVyTp06pbY7fPgw8+fPp2TJkqSnp2NhYYG9vWpoV3h4OGXKlNF5opEs4CZG/UaTFeiHnn1ZFClJKOM158FkBd1D0rg1WX7XEFnaYFDHGenpfeiVr4LEpR2pK6Yjti2JqIg1iqg8ch/ewXWfm4yd9R13rtylfOVyJCUkEf1Ss6YzB3yYsngSiXGJ9B7hxqWTKgfA/0YA7Xu3JeTBYypULU+V2pV5FBBEiVLFadvDlaunr2FT3IambZvgvXnfB2t9gzzED0n7QeiF3kdcvBTKtGSUSXFa7fXK10ARFgIf8aIhfxyApM3X6D17iNjWAWVaCsqkeO2aylZHEfH4o2oqLPpk/jcwHvQdWQ/uIi5VDmVe58WjAAyauiK7c1V1XtRrQuaJvR9VX9OqpVl28CoNHe15HBmPpYkRdlaaHRB7awu8rz7g6+Y1uf8imvDYJKqXKsaVhSPUNutP3KKktfknH4ZbELg4VWTZ7jM0rFKWxy+jsTQ1wc66iEZb+6KW7D7vy8A2Dbn/9CVh0QnY2VhoLRfIH4ViGC6okkvbtm2Lvr4+SUlJWFhY4ODgQEhICGlpaRgbG6sTT0EVQdi0aRN79+5l586deHp6frKs4aVLl5Kens769eupWLEiDx8+JCgoCD09Pa5du8bKlSsB6Ny5MyNHjmTixIl4enoSGxtL0aJFcXJyIjY2lpSUFFq1aqVbcRlppK39FcOveqFfuSbp7gvRq1gNk4nzc5nKLp1AERuF+dK/MZu9jsyjO1G8CkN29TTyiGeYL9uByXczSVs/H+RZHywpLSWNX0b9St9ve1HbuSa/jVtAjfrVWLIttyb/G/fYtcGb8b99T1pqGltWbANg2+/bMbMwY7+vF0s9F7L8p1WkvE7h6K4TvAp7hdf17Ww+vp6/1+zgxZOwD9aqJjOdTK+VGDTphF7ZamTuWYO4VCUMB2pOMNUrWw15eEj+95sX0nQy967GoHEH9EpXJfPAesQOjhj2naJZU5mqyCMef1xNhUVfehqpq+Zg2LE3+lVrkbZmPnqO1TGdtjCXqfTccRQxr7BYvRPzBRvJOLgDxUsdHFN5YGYkYdnQr/C84I9v6EsWDHTF7+krxm46msv2h25NCIqIpf+KPaw7cYs5/VppHC3z/4KZsSHLv3PD8/R1fINesHBkd/wehzH29125bKf0bUtQWBT95m5m3cEL/DqsCyaGEq3lBYkyn5/PAZGyEIzlSUpKonv37hw6dAgzMzPmz5/P/fv31fWhoaFUqlQJT09PEhIS8Pb25sSJEyxevBhHR0fi4+MZP348r1+/xs3NjV69emFmZkaHDh0wN88OxQUHB1OpUiVAFQFp1KgRs2bNAuDWrVuMGzeO0qVL59IXFhbGihUrcHZ2JioqikmTJrFjxw769+/Pzp07kcvl6OnpsX37dpycnPD09GT69OlYW1vz559/cuDAASwtLZHJZPz9999MmzaNjh07cv78eQB+/fXX9x4J83pYmw9t5o9O51Of96F2YnjurPjPBkmhCVZ+lsgefHiE7mNjOLDbu40KEP1GXQtaQp7IA68UtIQ8MWo26KNst6l963ytfyXCR0dKPpxCcVULCQlhwIABmJmpQo4zZsxQ1x06dIg///wTT09PsrKymD59OvXq1WPnzp0YGqoyn62trfH09OTw4cNcuXJFnbB6/Phxrfu8ffs2hw4dUi9LpVJatmypdRju25OHjRunmkhLX19fPVoHIC4uDk9PT5YuXaq2lclkTJ06lebNm9OjRw9GjRqFq6srbdq0oXXr1kybNo3ExERsbGz+c7sJCAgICHyZfAmjYApFBCQvoqKiMDQ0xNLSsqCl6ITMzEy14/QhCBGQD0eIgHy5CBGQD0eIgOSPjxUBaVyyZb7Wv/7yvC5k5ItCf1UrXrz4u40KEflxPgQEBAQEBAoLhd4BERAQEBAQ+H/jS+iCERwQAQEBAQGBQoYwEZmAgICAgIDAJ6eQp28CggMiICAgICBQ6PgSumAKzURkAgICAgICAl8OQgREQEBAQECgkCF0wQgICAgICAh8cr6ELhjBAREQEBAQEChkfAmjYIQcEAEBAQEBAYFPjhABERAQEBAQKGQohBwQgc8NSdfP910wHX3uv9uoANHr/Bm/k0P/856iX2RoWtAS8kTcILCgJWhF7FC1oCXkyef+rhW9qk0LWkKB8CV0wQgOiICAgICAQCHjS4iACDkgAgICAgIChQxlPv/+C+7u7vTo0YPhw4cTGxur1e7AgQN069aNQYMGERoa+s7tChEQAQEBAQGB/zOSkpJISkrKVW5hYYGFhYV6+c6dO/j4+LBnzx5u3rzJ77//zty5c3OtFxYWxsaNG9m7dy/R0dHMmzePzZs356lBcEAEBAQEBAQKGfntgvHw8GDNmjW5yseOHcu4cePUy1euXKFz587o6enRuHFjFixYoHF7t27domXLlpiYmFC2bFliYmKQy+Xo6elp1SA4IAICAgICAoWM/CahDhkyhB49euQqfzv6AZCamkq1atUAEIlEpKWladxeamoqJUqUUC8bGxsTHx9PsWLFtGoQHBABAQEBAYFCRn4jIP/uatGGmZkZ6enp6uWUlBStdtHR0erl1NTUd04XLyShCggICAgIFDI+VRJq7dq1uX79OgDPnj3DyspKq92NGzcAlZMSHR2NtbV1ntsWIiACAgICAgICGnF2dmbNmjXMmzcPX19fBg0ahLe3N0ZGRnTp0kVtV65cOczMzJg+fTrh4eG4ubmhr5+3iyE4IAICAgICAoUMpVLxSfajp6fHtm3b8PHxoUOHDtSrV0+r7apVq7hw4QL6+vo0a9bsndsWHBABAQEBAYFCxqd8G65EIqF9+/bvtBOLxbRq1eq9tys4IAICAgICAoWMdyV4FgaEJFQBAQEBAQGBT44QAfk/xPv6Izac8aN4EROWft0Ke2tzjXayLDnTdpznXlgMliaGzHZzwal0MWRZcn72usiFwDBszIyZ7daUxo4ldaavztetcZnYk+RX8ewf8wevw2I02okN9Oi6agyObeqQEvOa4z9u5tmVB+p6+3qOdFg0nD/b/4xSrvv+0t1nb7Jhvw/FrS1YOrYfDraaM76j4l8ze/N+Ql68oqJDcX77tifFrYvoXA/A7tPX2LD3FMWtLVk6cZB2TXGJzHb3JuRFJBUd7PhtdB+K21iq6+e4e1Onclm6tWigW30nL7Le6xjFbSxZNmUEDsWLarR7FZvAnHV/E/wsgoqlS/Lb2EHYFVVl3x+9cJMD565hYWrC+K+7UaakrU60eV++x4bjNyluacbSb9pjb6P5N5JlyZm29QT3nr3C0tSY2f1b41TWjtikVDrO8cDcWPXiwLoVSrL0mw460Qafd9sB7D7vy4ZDFyluZcHS0T1xKKZ5tIQsS87Ujfu4FxqBpZkxs4d0pmZ5e63ln5IjJ324ftuPeTMmf9L9fgifsgvmY/HFR0A+lzBVampqQUsAIORVPO4+/uwc35UfuzZm4cHrWm333grG0c6KMzP6MaFDfebsvQzAtksP0NcTc2ZGX8a0rcP8A1d1pq9YZQeaju/Olk4zOT17G1/9NkSrbaORHVFkZbG6wVgurdhLu/nD1HUisYh2c4fiM3/HR3E+QsJe4X7wHLvmjuGnQZ1ZtO2IVtslfx+jdb1qnF3zE83rVGbWpn061wMQ8iIS9/1n2LVgIj8N7caiLfu1a9p2iNb1a3B2/Sya163KrI271XVrvU9y5JKvzvUFP4/A3fs4Xst+YvqIPizc5KVd3197aN2oFj5/LaJ5/RrMXusJwN3AJ3ge8WH6iD7Uq1aRGas9dKIt5GUs7idvsXNaX37s1ZyF3he02u69+gDHEjacmTecCV2bMGfnWQCCwmPp0rAKZ+cP5+z84Tp1Pj7ntgMICY/G/chlds0awU8D2rFox0mttnsv3sXRvhhnl09kopsrc7YeybP8U3H+yg3mr1j32dwz3oVSqczX53Og0Dgg8fHxzJ49O0fZ7du3+fHHH7Wuc+rUKa3Txr6LJUuWcOVK9muopVKp+n+FQoFMJuPu3bv89NNPWrfxZoiSTCbD1dWVuLg4rbZr167F39//g7T+F849eEGXuhWwtTChVhlbEtMySJfKNNqGRiVSs4zqCal+eTuiX6tmwKtUwoqfuztjYWxI86ql1OW6oFK7+tzbe5mU6EQi7jzGxMoMA2PNr6KPDnzByZlbyUxK4/HZu5iXyH7arze4Lakxrwk9H6AzbW/j4xtIl6Z1sLWyoJZjaRKS00jLkOayk2VlcSUgBLdW9QHo1KQ2/iFhH0fT7ft0aVYPW+si1KpUloTkVO2a/INwc22k0uRSF//g5wA8CY/idUoa7Zxr6VzfuRv+dGnZCFtrS2pVLv+Pvszc+mRZXPF7iFsbFwA6t2iIX9BTAFLS0pk1egDlHUrQvbUzz19G51r/g7QFhNKlQRVsi5hRq1wJElMzSM/Ucl68iqdmOTsA6le0JzpR9XDxKDyGyvbaZ33Ml77PuO0AfO4G0cXZCVtLc2pVcCAhJY20zNzHHkBoZAy1KjgAUK9yaaITk/Ms/1QcOHqaUUP7f9J95geFUpmvz+dAoXFAlEolgYGBAPj5+eHt7Y2hoWGe44xdXFx49OgRmZm5T9Q39X369KFPnz44Ozvz5MkTdZ2BgUGObY8bN47Ro0dTr149xowZw19//cXJkydp0qRJjm1GRkZy8+ZNQDUzHMClS5do2rQpNjY2Ob7P2zRs2JBJkyap1/1YRL1OxdEu+0ZdzNyElwmaozMV7aw47PuY1EwZu68/UnezNKtSCnMjCQDXH7/EqbTuLrrmdtbEPMq+QSdHJWLhYKPR9sk5fzKTVTP0lXWpwcu7jwEwKmJKs8luGJgY0mP9eMq61NCZvjdExb+mUuni6uViVuZExiVqtM2Sy0lOzQAgJPwVVuYmOteTrSl7KuRiVhZExibkoUnVdiFhr7AyNwWgvL0tPw/rgVgk0r2+uEQcy2SH1G2tihAZE69ZX5ac5H+mfA55/lKtr1m9GlSrUJqMTCkbvY/T1rmObrQlpuBon92lUayIKS/jNd8AK5aw4fDNR6RmSNl9+R6NK5cCVA6Ih88dWv28mUHLd/M8WnPbf5C+z7jtAKISkqjkkN2dU8zSnMi41xptK5a05dDVAFIzMvE+74tztXJ5ln8qVs6fgeV7zAz6ufAp34b7sSg0OSCity6IycnJvHz5kipVqpCWlsaxY8d49uwZLi4uzJgxA7E4p1/Vp08f9f8KhYIff/wRFxcX7O3tmTFjBkeOHMHR0ZGHDx8ydepU9PX1efXqFSdPnqRVq1b8+OOPbNy4EYBu3bqxYcMG0tPTadeuHX5+fqxbtw6xWMzYsWOxsrLi9OnTNGzYUL1PLy8v4uPj6d+/P6Ghodjb29OrVy8GDBigtmnQoAGrV6/m+++/5+jRo2rnRdcoFEpMjQzUy8YSfZI1PEkBfOVUlj/P+TN2y2kiE1JYPqh1jnpplpwNp+8ypXNDjet/CCI9MZkp2dP+ytIzMbIwzXMdPYk+zSb35Oxv2wGoM9CVrEwZvh6nMLQwpefGCWzr8SuxweE606lQKDE1NlIvGxtK1E7G2xjo69O6XjUmrPobl5qV8Pa5SYfGNXWm452a0tJz2Rno69O6QQ0mLN+KS60qeJ+5RocmtYGc55mukSsUmL2tz0hCcmru6JmBgT6ujWoxYeFGmtathvfJS3RoVj+HzYzVHlz0vY/H/B90oi33eWFAcrqW86JuRf48fZuxGw4RmZDM8uEdAXAoasE3betRtZQtHmfvsGz/Zf4Y1UXjNv4rn3PbwZtjLztSaSwxIDkt9/kA8FWDqmw+dpnvf99FZNxrVozplWf5p+JjHvsCmikUDsjRo0dxd3fnxYsXdO3alfHjx/Pw4UP8/f158uQJpUuX5s6dO4wePZr9+/ejr6/PqlWrqFixIp07d86xLblcrv5fJBIhl8vVEZJatWrh5eXFxYsXGTduHH/++ad60pWTJ0/SqFEj9boeHh706dOHsWPH8ttvv9GhQwcaNGjAjRs3crz9Lzg4mKCgIDZt2oSjoyO9evXCy8tL4xsCjYyMSEhIYPv27YwaNUqnbfgGcxNDktOzQ6OZMrnWp91lR28ysUMDOtQuT0JqBsM3HmPHuK4YGagOm3Wn71C5pDVNKzvoTF/G61SMLLIjBPpGBigVeedwNJvsRvSDF4ReUHW3lKxdgWvrDhN4RDUtsH2dilRoVUunDoiFqbE6ggCQKZUhFmtux7nf9mT/BV/8H4eRkJzGgHZNNNrpRNNbDkemTKb1t507ui/7z9/EP/g5CcmpDOjw7kmD8q3PzCRHm2VIZYhEmoOwc8cNZv/Zq/gHhZKQlMLXnXI6v8unjuSaXyA/LNvEsXW/5fvmYW5iSHLa2+dFlvbzYv9lJnZrSod6lUhISWf473vZMbUfE7o2Vdv0aebE+mM38qXpbT7ntgOwMDXK4XDk2X5ep5nUy5UOjWqQkJzGN0u2sfOX4VrLjSQGGrfz/87nkseRHwpFF0ynTp3YsmULjo6OzJ07lwULFqBQKOjevTsuLi4MHz4cUE2C8qbbRCqVYmCQ+8DV09NT3/xFIhFisRgjIyP1+gYGBhw7dozmzZsjFouRSFRdDQcOHCA5OZn09HT69u1LkSJFOH/+PGlpaYSFhWFrqzmbfMuWLfTt2xcPDw+USiUKhULr64k3bNjAjBkz8PLyIisrK3+NpoXq9jYEvFD1/SqVSgJfxmFroblL4N6LGMrbqkZGWJkaYSQx4GWC6kVEl4PCOX3vGT93d9apvlcBodjXrahetquEX3vqAAAgAElEQVRRluRX2kPZ5VvWpEqnRpyYuUVdJk1JJ+lldr5NVqaUjNe6TQKuVs4e/8eqriKlUkngs5fYWmkeTWQoMaBf28bYFDGjd+sGFLf+OGHeauUd8A95nq3paQS2WkbbGEoM6PdVU2yKmNPbtfFHG5XzNtUrlMY/+Gm2vtAwimsZaWIoMaBfhxbYFLGg91fN1CN0fB8+Ju6f3ADn2lVJSk4jXUuuwX/SVtqWgGeR2drCYrC11Bx5u/fsFeXtVCM8rMyMMTI04HlMAodvBqptohNTEYtFOrtJfM5tB1CtTAn8n0Rk63sRqfV8CAiNoHxJVbetlbkJxoYGvIx9rbVcQDMKlPn6fA4UCgfkbWrWrMncuXOpWbMm5cqp+gjlcnmuXJCEhARWrFhBz5491Z8aNWqQlJSktlEqlbx+/VrtgCiVSsLCwoiOjqZy5co5ticSiRCJRBgbG+Pl5UX//v2pUaMGfn5+6iiMJubOncuIESPw9/fn0aNHODo6arR7U9+7d2/q1KnD/v3aRzDkh6aVHfB58IKz95/hftYfSxND7Cw1d/fYW5vjfeMRT6MTOez7mPC4JEpYmvIiNolfdl9kUf+WWGhJEP1QnpwPoFK7+lRqX5+m47uTnpBCcqTmvm6rMsXpsmI0B8euITMpOxz94sYjqnVzRqQnxryENY5t6xJ2K0inOl1qOeLj+5Aztx6w8cA5LM1NsHtrGOu/SUxO48S1AIZ3aaFTHTk1VcHn1n3O3LzHxn1nsDQzfYemVE5c9WN4d9ePpimHvrrV8bnhx5nrd9nofRxLc1Psimp/WVVicgrHL99mhFv2DIy37gezdMse5HIF1/wCsbWxxMQo/8dg06pl8QkI5az/Y9xP3MLS1Ag7LTdQe2sLvC/f52lUPIdvBhIe+5rSRS3589RtboWEk5SWyaaTN2lRo5zOwvqfc9sBuDhVxOduEGd8H7HxyCUsTU2w0+LU2he1ZPd5X55GxnL4agBh0QnY2VhoLRfQjDAKpgAQiUQo3grJy2Qyxo4dmyPnAuDhw4ds2rSJffv2qT82NjY5Xj8sk8mIi4tTRy9kMhmLFy/mu+++ey8trVu3ZtGiRTRo0EB9oQkKCuLs2bOMGjUKqVSKvr4+BgYG1KpVi+nTp2uczjYjI4Off/6ZmTNnIhaLGT16NKtXryYqKuo/t8+7MDOSsOzrVnheeoDv01cs6NcCv+dRjN1yOpftD50aEvQynv5/HGLd6bvM6eWCscQAr2uBJGfImOhxFtd5u3Cdt4uYJN2MhJGmpLP/u9U0HNGB0o2rcmjCeuzrOdJ7y5RctnUHt8HQ3IRemycz7tYaxt1ag6mtJQG7L/A6PJYxl1cy7Mhcrq07TPyTSJ3oe4OZsRHLx/XH88QVfB89Y+F3vfELfsHYZds02m85chG3VvWxKfJxcnsAzEyMWD5xEJ7HLuIbGMrCsf3xC37G2MV/atZ06Bxuro0+qqac+oxZPnUknod8uP0ghIUTh+H36Anfz1ur0f6v/adwa9sUG8tsR2BY97YoFApcR0znjx2HWDz5G91oM5aw7JsOeJ7zw/dJBAsGf4VfaCRjNxzKZftDj2YEhcfQf4kX647dYM4AV4wNDZgzoA2/7fKhy2/bkCuU/NRLd87m59x2AGbGhiz/zg3P09fxDXrBwpHd8Xscxtjfd+WyndK3LUFhUfSbu5l1By/w67AumBhKtJYLfLmIlJ+LK5QHMpmMCxcuMGPGDOzt7Rk0aBDh4eG0aNGCAwcOMHr0aBQKBUWKFMHY2JiAgACmTZvGiRMn1NvIysqiY8eOnDp1Sl0WGBjI1q1b6dSpE6ampty9e5eAgABWr17NypUradKkiTrvY8yYMbRv356lS5dSs2ZN2rVrh7OzM61atWL9+vXqF++EhYWRkZGBo6Mj/fv3Z+fOnQBs3bqVlStXcvnyZczNsy8KUqmU7777jho1ajBp0iR1+eLFi7l48SLu7u7Y27//ZDzp+xd9WCN/AlZNvF/QEvJkkne3gpagHX3dRpp0jcgw70ThgkYRHvhuowJC7FC1oCXkiTL+ZUFLyBO9qk3fbVSAGBQt/1G2a22uOZr+vsQnh+hIyYdTKCIgp06dwsPDg9mzZ7Nz505KlChBREQENWvWZNasWdja2vLHH3+wb98+pFIp06dPZ/z48Tm2ERkZmStPQyQSqaMMc+fOpXjx4syZMyfX/rdt20ZgYCC3b99m9uzZLF68mJIlSzJ06FB++OEHli1bxq5du0hOTqZUqVLqbhalUolcLmfr1q3s37+fn3/+mREjRnD9+nWUSiXBwcH069cPW1tbJk6cmGOfU6ZMoVq1anTp0uWTzA8iICAgIFB4+BK6YArFKJhOnTrRqVMn9XL16tX5888/cwyvtbW1pVu3bnz//fe0adOGjh1VQ+NiYmJYs2YNvr6+6onBAEJDQ5k6dSpr1qyhTJkyBAUFYWFhgbW1NcePH+fs2bN07doVgL59+zJo0CB1N4u3tzfe3t4sX76cKlWq0LZtW2bPno2dnR0tW7ZU7yMlJYVp06ZhYGDA9v+1d59RUZ3d38e/Q1XATmyIJZZg70lst4o1igg2sIFijUZEgzFoVCyxxcQWFXtvsfcWNUbsGgMGewlCFBEFEZBhGM7zgoe5RdE7/5Q5M7g/a7HWzDks/YXgzJ6r7Gv9ehwcHPjggw+YMmUKgYGBODk50bJlS4YMGfLaf7OlpSUzZ85k165d1Kz5zzeFEkIIYb5MZSHp32EWUzB/Vnp6OqGhodmKAEVROHDgAJUqVaJChf/urtDpdNy/f5/y5cu/9udERESg0+moVatWjn9PRkaGYVHqn8n0tmZp/zSZgvnrZArmr5MpmL9OpmD+nnd1Cia//d/7cxOT7/5DSf46sxgB+bOsrKyyFR+QOc2SNRryMmtr6xyLD8gcYXmbVxud/a9MQgghhMhO3h2FEEIIM2Mq57n8HVKACCGEEGbGVM5z+TukABFCCCHMjIyACCGEEMLocsP+EbPoAyKEEEKI3EVGQIQQQggzI2tAhBBCCGF0uWEKRgoQIYQQwszkhgJE1oAIIYQQwuhyVSt2IYQQQpgHGQERQgghhNFJASKEEEIIo5MCRAghhBBGJwWIEEIIIYxOChAhhBBCGJ0UIEIIIYQwOilAhBBCCGF0UoAIIYQQwuikABFCCCGE0UkBIoQQQgijkwJECCGEEEYnBYgQf9OTJ0/eeC8jI4MtW7YYMU3OzCHjy65du6Z2hDcypWyKohAREfHW+0+fPjViotf/flPO97+Eh4erHSFXkwJEGBw7doz//Oc/tG7dml9//VXtODkyxYzDhg0jKSmJ0NBQzpw5k+3r3Llz7N69W+2IZpFx8uTJREVFARAUFERiYiIAOp2O+Ph4RowYIdlekZGRwYwZM1AUBS8vL/z8/PDz86Nv376sWbOGCRMmMH36dFWymUO+t3nw4AF+fn4m8W8jt7JSO4AwHSEhIezZs4dnz54xceJEli9frnak15hqxuTkZJYvX05kZCQff/wxDx8+JCoqCjc3NzQajdrxANPPePz4ccqVK0fDhg0pXrw4AwcOJDU1FSsrKz755BPKli0r2V5haWnJ48ePuXz5MnXq1OE///kPAGXLliUpKYlbt24xY8YMVbKZQ743efz4MQMHDsTV1RV3d3e14+RaMgIiDGxtbSlQoAClS5cmLS1N7Tg5MrWMWUPI9+/fp0mTJpQqVQovLy+aNGlC6dKlCQgIQFEUyfgnFC9enJYtWzJt2jSGDh2Koijs3LmTrVu3smvXLvr06SPZcqAoCocOHUKj0fDHH3/w888/M3XqVMaNG0ezZs1ULy5NPd/LMjIy2Lp1K507d+Y///mPyY7O5BYyAiIMYmJiWLlyJYqiGB5n6du3r4rJ/svUMqalpfHs2TPmzp1LixYtAEzqBRXMI2NiYiJ6vR4vLy/q169P5cqVDfcSEhJo2bIlBQoUkGyvSE9P57333uOLL76gbdu2lC9f3nCvcePG7N+/n8ePH+Pt7S353iIlJYV169bxww8/ULt2bVasWEGFChVUzfQukAJEGHTs2JHk5OTXHpsSU8toa2tLuXLl+Pbbb5kzZw7x8fFcuHCB27dv8/TpU44ePar6m705ZBw+fDhpaWn89NNPrFu3jnnz5qHRaFi/fj3nzp1j+PDhki0HlpaWVKtWjQMHDrBu3TpsbGwIDw+nVKlSvPfee9jb2zN48GA6deqEjY2N5MvBxYsXCQwMJCEhgbFjx9K1a1dVcryLpAARBp999hm///47p0+fJjExkYIFC9KoUSOcnZ3VjmZgihkVRcHW1pZixYrh5OTEixcvcHJywsnJyWR2TJh6xsWLFzNr1izmz5+Pv78/vr6+pKWl0blzZ0qXLs2oUaNYunQpRYoUkWwv0Wg03Lhxg06dOjFp0iR69erF8uXLKVCgAI8fP0aj0aDValV7czf1fOHh4Xz55ZcsWLAAW1tbFi9ezJo1axg/fjz169dXJdO7RAoQYXDkyBEmTpxI8+bNKVy4MA8ePCAkJITJkyfTpEkTteMBppdRq9VSoUIFHBwcCAgIeO2+Xq8nNTXV6LleZg4ZbWxs6N27N3ny5OHy5cv4+fkxd+5c8uTJQ5MmTbCwsGDJkiUEBQVJtpecOHGCiIgISpYsSWJiIrt27cLFxYXr16+zadMmevfuzcaNG42ey1zyhYaGMn36dKpWrQrAN998Q1hYGGPHjqVVq1aqjm69C6QAEQbLly9n+/btFC1a1HAtLi6OgIAAkylATC2jRqPB2dkZDw8P9Ho9efLkyXY/PT2d5ORkAgMDjZ4tizlkzNrKunTpUhYtWoSHhwd2dna4u7ujKArTpk3j0qVLku0VZcqUwdnZmWXLlvH7779TsWJFIiMjSUlJITQ0lGfPnnHq1CkaNWok+XIwZMiQ167VrFmTrVu3MnToUMaOHcvXX3+tQrJ3gxQgwsDa2jrbGzuAo6MjFhams1nK1DLa2NgwcOBAunTpwpgxYyhRogRBQUGqDSnnxBwy/vjjj4wYMYJChQoxb948fHx8mDFjBuXKlTN8T8mSJdFqtdja2kq2/69s2bLky5cPCwsLunTpQmxsLAMGDGDYsGHcunULDw8PIiIiVHuDN/V8b5InTx4WLVpE9+7dCQkJYfDgwWpHypU0iinsvxMmoUqVKq99OobMIfy3dTM0psqVK5M3b97Xto2mpaWpnlFRFI4cOULr1q1VzfE25pAR4MWLF+TNmzfbtfT0dKys1P/MZGrZwsPDSUhIoEmTJly6dIl69eqxd+9e3NzcVMnzKlPP9za3bt0ib968lCpVSu0ouZIUIMLg2rVrzJw5k9GjRzNp0iRiYmIAyJs3L/v27VM5nXgXZGRk5Diade/ePRRF4f3331chVSZTzvYm8fHxFCpUSO0Yb2Qq+TZt2vTGrcDnz5/nww8/NHKid4P6HyeEyZg0aRJBQUFUrFgRS0tLjhw5wm+//UZISIja0QyuX7/OzJkz+eKLL5g8eXK2Imnv3r2qZLp48SIzZ87E2to623VFUdDr9TRr1oxPP/1UlWxZzCEjwNmzZ/n999+pVq0aH3zwgWFK4+TJkyQkJODv7y/Z/qQ5c+bw8OFDk+w0CqaVb8uWLTkWIOfOnePbb7/lhx9+UCFV7icFiDDQaDTUqFEDAH9/fywtLalRo8ZbDzIztokTJxqKJAsLCw4fPsxvv/3GokWLVMuUlJRE48aNGTp0aLbriqLQrl07k3hjN4eMs2bNIjo6mkaNGvH111+Tnp5Op06d6NmzJ2fOnFF1kawpZ8vKd+fOHcOC49q1a/P06VN69+7N06dPKVy4sOR7i+joaLp160Z6ejoffPABbdu2pW7dusyePZuxY8eqmi03M53VhUJ1DRs2pG/fvqxYsYLIyEhWrVpFnz59+Oijj9SOZpBVJFlaWmYrktQ+UTM5OZlHjx699pWens7Dhw9VzZbF1DOeOHGCXr160a5dOwC2bt2KRqPB29sbe3v7bF00JRuGHGfOnOHq1auMGzeOMWPGEBYWRps2bZg0aRJhYWH4+Pioks0c8mW9bpQvX54ffviB7du34+vry5w5c2jSpAmdO3emZs2aquXL7WQERBh89tlnXL58mdDQUCIjI8mXLx/9+vUzHCBlCrKKpCZNmpA/f34iIiI4fvy4qkWSo6MjMTExzJ49+7WOorVr12batGnMmzdPpXSZzCGjRqOhaNGi2Nvbo9FoiImJITY2lrS0NNXPqjHVbE5OTuzatYvffvuNkiVLZst769YtevbsycGDByVfDlJSUmjfvj2NGjVCr9dz584dVq1aRVhYGDVr1qRjx45s2LCBdu3aYW9vr0rG3E4KEJFN7dq1qV27ttox3sgUi6Rq1apRqFAhYmJiyJcvn+HLwcGB/Pnzm0QPFXPIWLZsWQICAqhWrRo6nY45c+bQsmVLAgIC+Pzzz1VdDGiq2Ro0aECDBg2ynYOkKApLlizh6tWrfPfdd6q22TflfHZ2dpw8eZKTJ0+yefNmBgwYgJeXFxMmTMi2o2nKlClMmzZNlYy5nRQgwqzExsby/vvvU7lyZWxtbVU/wyTL9evXmTdvHs+fPyc5OZmkpCSSk5N5+PAhAQEBJrGIzdQzzps3j6ioKMLCwihfvjw+Pj6G/79eXl5s3LhRtQLElLNdu3bttVGYFi1aMHDgQADVT4025XxWVlY0b96c5s2bc+vWLVauXImlpaXhvo+PD1988cUbd0CJv0cKEGE29Ho9rVu35oMPPkCn05GWloZerwcyP1V5eXkZ/UTcrMWvsbGxnD17lsKFC+Ps7GxYzJuRkcHt27eNmulV5pAxi7+/Pzt27GDChAmcPXuWBg0aAFC3bl1V14CYarZt27axceNGw7+DLPfu3WPUqFHkyZPntZ1PxmTq+dzd3cmXLx+AoUjq2bMnOp0OGxsb9Ho9ycnJUnz8S6QPiDAber0eNzc3Dhw4AMCTJ0+4d+8e9erV49mzZ3Tr1o1Dhw4ZNdOBAwdISUkhJCSETp06kZCQQHR0NNeuXaNy5cp4e3urPr1hDhnd3d2xs7Pj1q1buLi4EB8fT0pKCk5OToatwn5+frRp00ayvSQ6OpqiRYsycOBAUlNTURQFjUbDsmXLePHiBe+9957RM5lTvjcZPHiwSbUfyLUUIcxEenq68sknnyiKoih//PGHsmPHDsXb21t59OiRotfrlW+++Ua1bG5ubkp6errheXp6unL69GmlR48eipeXl6LValXLlsUcMnp5eRkee3p6KqmpqYqiKMrp06eVkSNHqhVLURTTzvaqiIgIpU+fPkrPnj2VM2fOqB3nNaaU7/z588rkyZOVqVOnGr5atGih3L59W9Vc7wIZAREm78WLF/Ts2ZO2bduyfft29u3bR79+/fjyyy/R6XR89913rFy5UtWMqampObaxVxSFc+fO8fHHH6uQKjtzyLh//37DVtft27fTqFEjihUrhlarxdraWtWhcFPO9iaxsbEAr52fZCpMIV9MTAz37t3Ltvbjxo0buLq64uTkpFqud4EUIMIs3L17l3PnznHkyBESExMZNGgQrVq1AmD8+PG0bt2axo0bq5bv1KlThjeg8uXLm+QLvjlkzJK1FdLUJCUl8eTJE8qUKZPtenh4OKVLl6ZgwYIqJfuv1NRUNBqN0Q/GM2cHDhzgk08+ee16UlISly5domnTpiqkyv2kABFmJSMjg40bNxIbG8uIESNISkri/v37VKlSRZU8SUlJaLVa/P39KV++PDqdjgYNGjBnzhzs7OzQaDQkJydz7NgxVfKZS0aATp06sX37dgA8PDzYuXOnqnlycv36dXbs2EGfPn0oUaKE4fpXX31F69atVdkOPmzYML766it0Oh03btzgyZMnFCxYEBcXFywtLUlLS8t2cq+xhYWFkZKSkuNhfRkZGeh0OlU/PAC4ubkRHBxMsWLFcHZ2NlzfvHkzR44cYdmyZSqmy71kF4wwCzqdzjDMvXnzZnbv3g2Ara0tM2fOZNWqVarkunbtGlOnTqVGjRpcuHCBPn36AFC8eHE6duxIuXLlVG0Tby4ZAezt7dm1axd169Y1me3VWbZu3YqVlRV3797FwsKCNm3a0KhRI0aMGEGFChW4cuUKEyZMUCWbVqvlypUrvHjxgvXr1+Pl5YWdnR1dunShatWqREREcP78eVWyAUydOpVKlSrl2LDt+PHjuLq6ql6A6PV6Dhw4QFRUFHfv3qV+/fq4ubmxceNGFi5cqGq23EwKEGEWWrZsSZ48eVAUhYcPHxp2HCiKwrNnz4iKisr2ycVY6tevz9atW+nWrRtVq1bl6NGjtG/fPtv3qP1mag4Zs8THxzNo0CCTaA3/soULF9K1a1fy5s2Lu7s7YWFhfPrpp4wbN45ixYrRqlUr1baTWlhYoNFosLGxMTy2tLSkYsWKrFy5kh49eqiS62WTJ0/O8bqXl9cb7xlDUFAQjRo1omDBgowbN44XL17w66+/Mn36dPbu3cvnn3+erYOr+GdJASLMwokTJ4DMIVsPDw/DCAjAhg0b2LNnD0OGDFEl28mTJ6lfvz6lS5c2LGQzlTf0LKaeMSwsjMTERPr06YOPjw8dOnRQO1I2JUqUyHZgX9aZRF26dGHixIl07drV6Jm0Wi1z5swhNTWVgwcPkpGRQUxMDMeOHaN79+5Gz/Mmb/s9U/t30MvLi0OHDvHgwQMWLFjAiRMnqFatGoGBgbi4uNC3b18aNWpExYoVVc2ZW5nesm0h3kKj0RAUFJTtWuPGjalUqRL37983ep6LFy8SERGBTqfj7Nmzhk/BkZGR7N69m+XLl3Pnzh2j5zKnjM+fP2fSpEkkJSUZrtnY2KiWJyeRkZFMnTqV48ePA/Dw4UMGDBhAREQEhw4dYsWKFUbPZGNjw4cffoi1tTUFChSgYMGChsfiz8k6ouDQoUNYWVnRuXNnxo8fT5MmTTh37hwTJ05k3LhxasfMtaQAEWZFo9EYOlBmKV26NJcuXVKlcVC9evUYOnQoz549w9ramlOnTlGyZEkWLFhA9+7dCQwMVH19halnzJcvH9u2baNkyZIcPHgQDw8P0tPTVcuTk++//55GjRqxc+dOZs6ciY2NDVOmTGHs2LEUL14cFxcXfvnlF6Nm0mg0NG/eHEtLSxo0aMCHH35IkSJFqFu3rlFzmDNLS0t+/fVX8uTJw6BBg9ixY4fh3qFDh3BxcaFLly4qJszdZApGmL2jR49y4MABww4KNcyaNYvu3buzceNGw7Vvv/2WNWvWqJbpVeaQUavV8v333zN8+HC1o2RTq1Ytrl+/zty5c7l58yZDhgzBwcGBxYsXExERQZ8+fVQ5FTcgIICYmBiSk5NJT08nIyOD5OTkbOerqD3NYco0Gg3nz5/H09MTyDwh19PTE0VRePr0KefOnZMC5F8kBYgwa6tWrWLjxo2sWrWKwoULq5LBzc0Na2trIiMjDS9kiqIQFRXFnTt3VD/DBMwjY2pqKh07dgQys40dO5ZChQpRpUoVmjVrhp2dnSq5dDodAQEBVKhQAXt7e0aMGGGYIlIUhVu3bqHValm8eLFRcymKQteuXVm6dCmjR4+mXLlyODg4sH37dho0aMD9+/dxd3fn6dOnhl1kalAU5bVp0yyRkZEEBQWpetps/fr1WbRoEc+ePeP58+eGgu2XX37h559/xtXVVbVsuZ30ARFmJz09nWPHjrF06VKqVq1KYGAgDg4OquXJenHv0aMHGzZsMFzfs2cPd+7cISAgQLVsWcwhY3R0NKVKlQLg6tWrpKSkEB8fz/nz5zl+/DiDBg1SZbHn3bt3OXXqFL179+bOnTvs3r2bESNGGO5rtVpiYmJea05mTDt37iQkJIQZM2aYXAO38PBwXrx4kWMfEEVRSEtLo2HDhiokyzyJ19/fn5CQEA4fPszOnTsNzfoUReHatWuq98fJzaQAEWbBw8ODhIQEdDodiYmJ2NvbM3PmTFUaP73Jpk2b8Pb2Njx/+vQply5dMnRsNQXmkDEnjx494tKlS4ZW6GqJi4vjxo0bNGrUSNUcOYmKiiJ//vyyCPUftHbtWry8vExuUXRuIQWIMAspKSloNBoyMjJ49OgRly9fZs+ePQAEBwdTtmxZdQO+gU6n49ixY6qclPpnmWLGrJN6TcmZM2f4+OOPc1xTcfHiRerVq6dCqsxP8YqiZGu9HhUVRcmSJbOdbyLebObMmYwcOTLHURrx77EMDg4OVjuEEP+LtbU11tbW2NjYGNYFeHh4ULRoUUaPHk2FChUMw/dq6d27N/fv38fZ2Zn8+fMDmX1LAgIC6Nmzp6rZsphyxsmTJ/P+++9ToEABBgwYQLt27bC1tTWMeo0ZM4a2bdsaPVdaWhqhoaHMmDEDJycnbGxsuH//PgULFiQ4OBhXV1d69eqFr6+vUXMpikJ8fDzDhg1j165dtGrVyvBJfebMmej1eipUqGDUTDmJjIwkJiaGuLg4EhMT2bdvH7t37+bnn3/m559/5ujRozRv3lyVbGfOnOHhw4esWrWKDz74gAcPHrz2lZaWRqFChVTJl9tJuSfMWsOGDVm8eDH9+/dn/vz5VKpUSbUsI0eOJDQ0lAEDBlCyZEnmzZuHnZ2dSQ3fmnLG48ePU65cORo2bEjx4sUZOHAgqampWFlZ8cknn6g2yhUfH8+KFStITk7m6NGjREdHc/jwYZYvX05UVBSQ2UY+IyPDqCfiJiQkMHv2bABatWqFra0t06ZNIygoiLCwMAIDA42W5W2OHDnC7du30Wg03Lt3j+DgYFxcXNBoNAQHBzNx4kTVsoWGhmJlZUVycjJ+fn40adIEFxeXbDuajh49ytdff02NGjVUy5lbSQEizJ6zszMTJkwgODg42wJLY0lISMDOzo6qVatStWpVBg4cyNmzZ7GysiItLc0ktkGaQ8bixYvTsrtCTEsAABe7SURBVGVLxo0bh7+/P1OmTDEcSOfu7s7atWtVyZWamkr9+vXJyMigU6dOhIWFGaYDAV68eAFg1OIDwMrKylA4RkZGoigKV69eJTQ0lOrVqzN9+nSmTZvGuXPnqFGjBvb29kbNl6V///6Gx15eXri4uBie29nZUadOHTViATBq1CgAzp49y4QJE9i3bx+XLl3i008/NfQbcnFxUW0HUW4nBYjIFRo2bKjacHObNm2yvbhrNJpsn6BMYVGgqWdMTExEr9fj5eVF/fr1s63/SEhIoGXLlqplHDlyJM+ePSMxMZHJkyeTlJREQkIC/fr14+bNm/Tr10+VXFmyGpJZW1vzySefsHTpUqZMmYK3tzfDhw/n7t27bN68WdWMWV4tdE2h8IXMnXVNmzbF1dWVqKgoZs6cyaZNm5g4caIq037vClmEKsS/zNPTM1uHRVOkdsa+ffuSkJDA9u3bWbduHY8fP+b8+fN06NCBc+fOMXz4cNV6lcTExLBt2zbOnTtHYGAgv/76Kz/++COrV6+mX79+hISE0LlzZ8OiaGN5/vw57dq1Q6vVUrFiRTQaDY8fP6Zp06bo9Xpu3bpFq1atuH79Ol9//bVRs2Vp3759tp4pkZGRhqm0rOeXL19WJVuWe/fuERERQc2aNbG0tCQjI8OwMLt48eLkz5+fJk2aqJoxt5IRECH+ps6dO2NnZ2cyn+ZyYuoZFy9ezKxZs5g/fz7+/v74+vqSlpZG586dKV26NKNGjWLp0qUUKVLE6NmsrKwoXbo0p06dwt7enjJlyuDo6IhWq0Wr1XL9+vVs0wrG1Lp1a+7du8fYsWN5//33mTVrFseOHeP7778nODiYO3fu4O7urko2gB07dmRbX+Tl5ZVtNMbLy0uNWNn06tULV1dXzp07l+26p6cnkydPxs3NTQqQf4kUIEL8Tf7+/hQsWDDbNkitVoutrS2KojBmzBgV02Uy9Yw2Njb07t2bPHnycPnyZfz8/Jg7dy558uShSZMmWFhYsGTJkjd21Pw3OTo6kj9/fsaPH0/58uXZsWMHderU4erVq0yfPp2QkBBVGqRlURSFjRs38sUXXxAeHk6bNm0Mp+P26tVL1S63ry5uNsUpmFKlSmFnZ4dWqyVPnjzY2tpStGhRUlJS+Pzzz2ncuLHaEXMtKUCE+JuaNm0KgF6v56effmLHjh38+uuvnDhxwmT6MJh6xvj4eEaMGMHSpUtZtGgRHh4e2NnZ4e7ujqIoTJs2jUuXLqmWb/r06RQvXpymTZty8eJFunXrxp49ezhy5AjOzs58+OGHRs+Unp6OTqdDo9FQqFAhNBoNhQsXZtCgQbi5uTF48GCT2Ia7c+dOHj9+bFhzZOzdQv+LRqOhQ4cO6PV6NBoNiYmJxMXFsXfvXmJjYylTpgzOzs5qx8yVpAAR4h/i6+uLhYUFnp6eTJ8+HUtLS3Q6HVqtVu1oBqaa8ccff2TEiBEUKlSIefPm4ePjw4wZMyhXrpzhe0qWLGkYtTEmnU5HrVq1mDZtGvv27cPW1pby5cuzfv16Bg0aRGxsLCNHjmTWrFlGfWMtVKgQgYGBDB8+nOjoaJ49e8bcuXOBzIIzq8+L2ooUKWIYCfn0008ZO3Ys165dAzJ/tmr7448/mDJlChqNxrC7Sa/XM3nyZACGDh3K3Llzs/0uin+GLEIV4h+SlJT02pk0CxYswNHR0STmuiHnjGlpaezYscNkMkLm1ta8efNmu5aenq5ap8r4+Phszaiyti5nbc88duyYaoeWRUVFkZycTPny5Q15oqKiKFy4sGpbb3OTe/fu4ezsLF1S/wVSgAjxN6Snp7Ns2TIGDx6c4/2OHTuya9cuI6d6u7CwMJM7sOx/SU9P59atW6q2Z8+pAIqMjFT1ELqcvHjx4rVpNVNoNJfl5SI4OTmZvHnzqj4lc/r0aRo0aGASa1LeJaYzESeEGdJoNBw5cuSN9/PkyWPENG/WqVMnw+MJEyaomOTN/P39adGiRbZh+fDwcAAsLS3p3LmzqkP2np6e2Z7Hx8fj4+PD3bt3VUqUs+bNm9O/f3/69evHxx9/zOrVq9WOxPr16w2N23r37m24vnXrVsaOHatWLIMZM2YYHiclJamY5N0iBYgQf4OlpeVbh2ZN5ROVvb09u3btIjo62mQyvSouLo6jR4+iKIqhudeUKVOAzJ+j2tuIX57OSE5OZsiQIfTv35/3339ftUwAGzZsYMuWLWzcuJHExEScnJxYs2YNa9eupXTp0gwYMEDVfJC5HTdrlOPlonzbtm0mcbJw1voPgAYNGtC8eXPatGlD7969Wbp0qcrpci8pQIT4m0z1Df1V8fHxDBo0iMjISLWj5Cjr52hjY2Nob/5ycafRaFSdh8+a1jh//jze3t506tSJkydPqpYny6pVq7CysuLMmTPExcVl+31U+3dz8ODB7NmzJ9sUS9bjdevWUaBAAdzc3NSKZ/Dyz6lSpUocP36c/fv3M3XqVHbv3s2dO3dUTJd7yaoaIf6m8PDwHM+zUBTFJBauhYWFkZiYSJ8+ffDx8aFDhw5qR3pNampqtufp6ek8evSI9PR0YmNjURRF9XUCWq2Wnj17Uq5cORYvXkzJkiXZvn27qpkAChcujKenJzExMQBER0fTvXt3NBoNDx48oEuXLmzdulWVbEOHDmX79u3cunUr2/UtW7awfft2VqxYoUqul6WlpWV7nlWMWFpa4uzsTN26dbl586aq/VRyK/VfHYUwc9WrV3/jWRtq7yx5/vw5kyZNyjavbUoLEgEuXrzI+PHjs+16uXv3LqNGjcLW1pbPP/+cpKQkKlasqEq+xo0bY29vz4MHDyhQoACPHz/mwoULKIpCbGwszZo148WLF6910lSDRqPh7NmzascwqF69OtWrV+fq1at4enqiKAq///47t2/fZuvWrRQsWFDVfFOnTuX69etv/Z7PPvsMR0dHIyV6t0gBIsTf9LZhbrWHwPPly8e2bdvo3bs3Bw8eZOHChZjaxreyZcuyevVqAgICDNcqVarEmjVrDM8nTpxIvXr11IhHaGgokNnOPiMjA1dXV/r27YuDgwPdu3dn48aNquR6VdYBg23atKFEiRJoNBqioqIoUaKEaicJx8XFsXLlShRFMZw11K9fPzp27MiQIUOYM2eOqiML9evXZ8SIEXTv3p39+/djZWVFcnIy0dHRlCpVCkCKj3+RrAER4m9QFAW9Xq92jD9Fq9Xy/fffm8S00MscHR157733DM8VRSEjI4MLFy5w/Phx1q9fT1JSEu3bt1cxZebiya1bt+Ls7EyPHj04dOiQ6qcIQ+ab/ObNm7ly5QoADg4OrFq1ipUrV+Lg4KBa8QEQFBT02jZlCwsL3N3dmT17NqNHj35tCsSYWrVqZRh5u337NmFhYdSqVYvPPvuMdu3acfDgQdWyvQtM65VICDOj1+v56KOP3nhf7Q6jWVJTU+nYsSOQ+QY/duxYChUqRJUqVWjWrBl2dnYqJ8QwpVG4cGGaN2+Ora0te/bs4cCBA4wcOVLteCiKgqWlJR4eHjRr1oxhw4bRsGFDtWPh5+eHlZUVDRs2pEiRIia1CDVrB8nmzZsNLdizRuAqVKhA69atWbt2rWHXk1o0Gg3+/v7ZroWHhzNmzBhu3LjB8OHDVUqWu8kIiBB/g5WVFYGBgW+87+HhYcQ0bzZ79mzD46lTp+Lp6UnNmjW5fPky7u7ubNmyRcV0mSpUqGCYhhk0aBA1atRg0qRJHD58mPDwcPr3769qQff8+XPD44IFC7Js2TKOHz/Ozp07VcsE0KNHDzp37kzPnj0pUKAAf/zxB927d6dHjx6GRahq8/b2NhQeWTucALp168bFixfVimWQnp5u6FOSpUaNGmzfvp2IiAhOnDihUrLcTTqhCvGOe/ToEZcuXaJdu3ZqR3mrrVu3qvpm+vIhagcPHqRt27asW7eOFi1aUKJECdVymbqnT59SuHBhw/Pw8HBq1KhheK7X61U/EPHKlStUr14927XU1FS2b99Ox44dyZMnj+oZcyMZARHiHXfjxg2TLz4AVYuP9evXZ9sGvGrVKlJSUli0aBHW1tYsXrxYtWxZ4uPjuXjxIomJiWpHySbrmIIff/yR0NBQxo4dy9WrV7lz5w4+Pj4m8cYeGhqKj48PPj4+9OvXj8WLF/Ps2TOuXr1KUFCQSWTMjWQNiBDvgP3797+xyPjyyy85ffq0kRO9rl69epQsWdLwYv/y4Kxer+fhw4eqDddHR0dne543b17mzJnDgAEDcHR05KeffmLQoEFGzxUXF8eUKVNQFIWuXbsaCqJVq1YBmdMdISEhjBgxwujZsmR1kP3mm2/w9fXlxYsXHD9+nJSUFG7evImnpyerV69W9fTesmXLotVq6d+/P+np6QwbNowuXbowZcoU1bfS52ZSgAjxDpg/f76hABk/fjyTJk0y3Hv1dFy1lCpVih07dnD8+PEcT5ZVaz1Nhw4dePr0KceOHTP0UImOjsbFxYVLly7RtWtX1c78SUlJoUiRIkRERACZ56ysXr0aPz8/dDodjRs3Ji4uTpVsWbIWwhYpUoS6deuyd+9ehgwZws2bN7l8+TLdu3dXbW3Pl19+ibW1NcnJyVy+fJknT54AULJkSRYtWsRXX32lSq53hRQgQrwDbG1tDY9fHe1Qe6dElqzzOEJCQnB1daV9+/ZYW1uTL18+VbeS7tmzhxkzZhAWFoa3tzdWVlZs2bKF0aNH07NnT+zt7VX7GWo0GhwdHQ27mKytrbG2tmby5MlkZGQwYMAAli1bpkq2V6WlpbF//350Oh0hISE4OTmh0WhU7czbtWtXw7+N2bNn89FHH1G2bFngvyNwpvLvIzeSAkSId8DLL6JqtzT/X7LyOTg4sHnzZpMZAn/5wDIAX19frl69ioeHh2qjDMOHD+fp06ckJyczbdo09Ho9cXFxFC5cGK1WS7t27QwNtdTg6+vLH3/8QWpqKlqtlmbNmnHy5Em8vLxwcHBg06ZNqmUDqFu3Llu2bCEmJgZra2v27NmTbTHqhg0bVJ0ayu2kABHiHfDyegpT/USnKApDhw7lxo0beHp6Gj6ZmmJerVbL6tWr8fX1ZcOGDfTt21eVHHPmzGHfvn1cuHABPz8/UlNTWb16NZs2beKHH37INtWmhgULFrB371769OlD4cKFuXDhAk+fPmXOnDkkJCRQrVo1VfNBZtfdEiVKUKVKFb7++mt8fX3JyMggLi6O+vXrU7VqVbUj5lpSgAghTIJGo2HBggV4e3uzadMmunbtSlJS0mv9GYzN1dUVrVaLvb29YbfL48ePGTx4MDqdDp1Op1qRNHHiROLi4njy5Alr1qyhadOmaDQa+vbtS7169QgMDGT27NlUqVJFlXwODg7UqlULNzc3Fi9ejJ+fH3nz5qVnz54mMxJXs2ZNIHON0Zw5cwxbhLt06cKGDRtM7uyk3MQ0fgOEEP+ql98g1X5Df5uMjAx0Oh0AdnZ2jBw5EicnJ1UzHTt2jPbt23P48GH27t3L3r17qVKlCh06dKB69epYW1tna65lTH5+fjRs2JAyZcrQqFEj5s+fz927d4mIiMDCwoJhw4axfPlyVbJBZg+Q4cOHEx8fz8iRIwkODsbT0xNfX1/WrVtHcnKyatkAgoOD8fb2xtvbm/v37zN16lS8vb3x8vIiOjqaY8eOqZovt5MRECHeAS+/QRYtWlTFJG+n0WgMW0ZXr1792j21uLi4ZHuelJSEh4cHixcv5v79+7i5uamSy8nJCXt7e2xsbLh//z4BAQF88803bN68GUVRaN++PZcvX1YlG8CWLVsICAjA2dmZtWvXUrBgQRwcHFiwYAE//PAD7u7u9OzZEz8/P1Xyvdx+3c/Pj0WLFhmeX7p0ibVr19K2bVs1or0TpBOqEO+A9evX07NnzxzvNW7c2HDiq5pq1aqFs7MzlpaWrxUbOp2O6OhofvrpJ9WPcIfMN6e6dety+fJlKleurNo23MePH3PlyhWsrKy4ffs2TZs2ZfXq1dSrVw93d3cg8/TZJUuWqN5M6/jx43z00UfZzh16/vw5t27dok6dOiomy/Rqr5z09HQOHz5sFk36zJUUIELkcjqdjrCwsDceZ793717VPsGLf96tW7fInz8/xYoVUzuKEG8la0CEyOW0Wi2LFi1Cq9Vy+vRpzp49S3x8PP369aNDhw6cOHECvV6vdkzxFzx8+JCYmBggs8+GXq+nfPnyvPfee2RkZJj0eh8hpAARIpezsLDg2rVrjB49mq+++oq1a9fy22+/kZqayp49e0hLS+OXX35RO6b4C65evYqvry9nzpzBx8cHV1dXatasiaurK7Vq1aJp06b07t1b7ZhC5EgKECFyudWrV1O6dGm+++47ypQpQ7du3bLd79ixowzXm6kWLVqwatUqw/qOEydOUL58eX766ScqV67MyZMnKVGihIxwCZMku2CEyOUcHR3RarWsWLEix50krq6uyFIw8zR79mysra0BDFMxr/4/njlzptFzCfFnSAEiRC7XqVMnbty4QaVKlVi4cCFRUVFA5kmqTZs2pWDBgqSmpnLo0CGVk4r/KwcHB0MBovYuFyH+r6QAESKXs7S05MqVK7Ro0YKaNWvi6+uLpaUlixYtIiMjg++//x5HR0e1Y4q/YMCAAYSHh6MoCgcPHiQtLQ1FUUhLSwMyd0ApiiLdPIVJkjUgQuRy169f586dO9y7dy/bgWoajYagoCAmTJigckLxVx0+fJigoCD0ej3FihWjU6dO6PV6OnfuTEpKCh4eHqqeNivE28gIiBC5mF6vJzg4GEdHR9zd3dm/fz+QecR8bGwsNWvWxNramnPnzvHRRx+pnFb8XzVo0IA6derg6OhoEs28hPi/kBEQIXIxS0tLNm3axODBg/Hx8cHHx4fq1avTrFkzevXqBUDv3r1Zv369yknFX6EoCn379uXKlSvodDrmzZtHamoqvXr1YsmSJYb1PkKYIumEKsQ7IiwsjOrVq792CqmiKMTGxspWXDO0bt06nj9/zqeffkpISAixsbGMHz+eR48esX//ftasWUOFChVYunSp2lGFeI0UIEIIkQscPHiQJk2aYG9vb7iWlpZGREQEtWvXVjGZEDmTAkQIIXKJ06dP07BhQ7VjCPGnyBoQIYTIBc6cOcOyZcvQ6XRMnTrVcP3GjRsqphLizaQAEUIIM6coCvPnz2fUqFFYWFhw8+ZNILMo8ff35+HDhyonFOJ1UoAIIYSZW7hwIR9//DHLli3D0tISKysrfvnlF7755hvq1KlDiRIl1I4oxGukABFCCDOVnJzM0qVLiY2Nxd/fn6dPnxruWVhYMH/+fJKTk1VMKMSbSSMyIYQwU7Nnz+a3335j06ZNwH8PotPpdNja2uLk5CQFiDBZMgIihBBmasyYMdStW5fZs2cDGE41fvToEV9++SWPHj1Cp9OpGVGIN5ICRAghzJSFhQWjRo0CYN68eeTNmxeAUqVK8e233zJ27FieP3+uZkQh3kgKECGEMHMjRozg6tWr9OvXD71ej16vp0KFCnh7e3P37l214wmRIylAhBAiFwgODuarr74iOTmZChUqANCyZUu2bdumcjIhciadUIUQIpe4cOEC9evXNzxfsmQJAwcOVDGREG8mIyBCCJELdOzYMVvxAXDs2DGV0gjxv0kBIoQQuYC1tfVr1ywtLVVIIsSfI31AhBDCTDVu3Bh7e3sUReHRo0e0adPGcE9RFBISElRMJ8TbSQEihBBmKjQ01PC4S5cubN26Ndv9Hj16GDuSEH+aTMEIIUQuoNVqX7um1+tVSCLEnyMFiBBCmLnIyEgWLlyIVqslKCiIjRs3AlCjRg2VkwnxZrINVwghzNSJEydITk7m8ePHrFixgn79+hEVFUXnzp0N35OWliaFiDBJsgZECCHM1LVr16hZsyaPHz9m1KhRHDx4kFOnTpGUlGT4nrS0NL799lsVUwqRMxkBEUIIM7dy5UpcXFxo0KABwcHBODg4EBgYqHYsId5K1oAIIYSZq1evHqVLlwZg3LhxhlbsQpgyGQERQgghhNHJCIgQQgghjE4KECGEEEIYnRQgQgghhDA6KUCEEEIIYXRSgAghhBDC6P4fhBxTX9q8+O8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(data=df.corr(), annot=True) # annot=True: 显示数字\n",
    "# 变量间呈现较高的相关性，需要进行变量压缩"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA 建模"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据标准化\n",
    "使用中心标准化，即将变量都转化成 z 分数的形式，避免量纲问题对压缩造成影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import scale\n",
    "data = df.drop(columns='area') # 丢弃无用的类别变量\n",
    "data = scale(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 初步建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(n_components=9)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# 说明：\n",
    "## 1. 第一次的 n_components 参数最好设置得大一些（保留的主成份）\n",
    "## 2. 观察 explained_variance_ratio_ 取值变化，即每个主成分能够解释原始数据变异的百分比\n",
    "pca = PCA(n_components=len(data)) # 直接与变量个数相同的主成分\n",
    "pca.fit(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 结果分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 累积解释变异程度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8.01228521e-01, 1.22511074e-01, 6.21541492e-02, 9.94725332e-03,\n",
       "       2.46475660e-03, 1.34733699e-03, 2.11552456e-04, 1.35356084e-04,\n",
       "       1.72250209e-35])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 累积解释变异程度\n",
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.cumsum(pca.explained_variance_ratio_), linewidth=3)\n",
    "plt.xlabel('成份数')\n",
    "plt.ylabel('累积解释方差'); plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 重新建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.80122852, 0.12251107])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 可知两个主成分就已经足够了\n",
    "pca = PCA(n_components=2) # 直接与变量个数相同的主成分\n",
    "pca.fit(data)\n",
    "\n",
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始数据集规模:    (9, 9)\n",
      "降维后的数据集规模: (9, 2)\n"
     ]
    }
   ],
   "source": [
    "new_data = pca.fit_transform(data) #  fit_transform 表示将生成降维后的数据\n",
    "# 查看规模差别\n",
    "print(\"原始数据集规模:   \", data.shape)\n",
    "print(\"降维后的数据集规模:\", new_data.shape)\n",
    "# 9 个变量压缩成两个主成分"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 主成分中各变量的权重分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pca_1</th>\n",
       "      <th>pca_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>0.353645</td>\n",
       "      <td>-0.211802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>人均GDP</th>\n",
       "      <td>0.034093</td>\n",
       "      <td>0.942455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>工业增加值</th>\n",
       "      <td>0.364282</td>\n",
       "      <td>-0.013027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>第三产业增加值</th>\n",
       "      <td>0.367803</td>\n",
       "      <td>-0.040553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>固定资产投资</th>\n",
       "      <td>0.365715</td>\n",
       "      <td>0.098109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>基本建设投资</th>\n",
       "      <td>0.352785</td>\n",
       "      <td>-0.032065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>社会消费品零售总额</th>\n",
       "      <td>0.366340</td>\n",
       "      <td>-0.123313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>海关出口总额</th>\n",
       "      <td>0.295610</td>\n",
       "      <td>0.051071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>地方财政收入</th>\n",
       "      <td>0.354963</td>\n",
       "      <td>0.191407</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              pca_1     pca_2\n",
       "GDP        0.353645 -0.211802\n",
       "人均GDP      0.034093  0.942455\n",
       "工业增加值      0.364282 -0.013027\n",
       "第三产业增加值    0.367803 -0.040553\n",
       "固定资产投资     0.365715  0.098109\n",
       "基本建设投资     0.352785 -0.032065\n",
       "社会消费品零售总额  0.366340 -0.123313\n",
       "海关出口总额     0.295610  0.051071\n",
       "地方财政收入     0.354963  0.191407"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 两个主成分与 9 个变量的系数关系\n",
    "results = pd.DataFrame(pca.components_).T\n",
    "results.columns = ['pca_1', 'pca_2']\n",
    "results.index = df.drop(columns='area').columns\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    1. 主成分1几乎不受 data 的第二个自变量 ‘人均GDP’ 的影响，0.034，其他自变量对其影响程度都差不多\n",
    "    \n",
    "    2. 主成分2受 data 的第二个自变量 ‘人均GDP’ 影响最大，达到了 0.94"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 结果描述\n",
    "目前我们把 9 个自变量压缩成了 2 个主成分，每个主成分受哪些变量的影响也有了了解\n",
    "\n",
    "虽然得到的主成分都没有什么意义，但我们是否可以通过变量们对主成分的影响程度\n",
    "\n",
    "来为生成的两个主成分命名呢？\n",
    "\n",
    "\n",
    "答案：\n",
    "    \n",
    "    第一个主成分在表达经济总量的指标上的权重相当，可命名为 “经济总量水平”\n",
    "    而第二个主成分只在人均GDP上权重很高，可命名为 “人均水平”\n",
    "    \n",
    "###### 重点！\n",
    "给两个主成分“命名”是对降维后的数据进行，\n",
    "\n",
    "而不是 pca.components_ 打印出来的个变量在成份上的权重"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pca_1</th>\n",
       "      <th>pca_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.353645</td>\n",
       "      <td>-0.211802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.034093</td>\n",
       "      <td>0.942455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.364282</td>\n",
       "      <td>-0.013027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.367803</td>\n",
       "      <td>-0.040553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.365715</td>\n",
       "      <td>0.098109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.352785</td>\n",
       "      <td>-0.032065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.366340</td>\n",
       "      <td>-0.123313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.295610</td>\n",
       "      <td>0.051071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.354963</td>\n",
       "      <td>0.191407</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      pca_1     pca_2\n",
       "0  0.353645 -0.211802\n",
       "1  0.034093  0.942455\n",
       "2  0.364282 -0.013027\n",
       "3  0.367803 -0.040553\n",
       "4  0.365715  0.098109\n",
       "5  0.352785 -0.032065\n",
       "6  0.366340 -0.123313\n",
       "7  0.295610  0.051071\n",
       "8  0.354963  0.191407"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 重建索引，让主成分的数据框的索引与最初的数据表的索引一样\n",
    "results = results.reset_index().drop(columns='index')\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>GDP</th>\n",
       "      <th>人均GDP</th>\n",
       "      <th>工业增加值</th>\n",
       "      <th>第三产业增加值</th>\n",
       "      <th>固定资产投资</th>\n",
       "      <th>基本建设投资</th>\n",
       "      <th>社会消费品零售总额</th>\n",
       "      <th>海关出口总额</th>\n",
       "      <th>地方财政收入</th>\n",
       "      <th>经济总量水平</th>\n",
       "      <th>人均水平</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>山东</td>\n",
       "      <td>10550.0</td>\n",
       "      <td>11643</td>\n",
       "      <td>3502.5</td>\n",
       "      <td>3851.0</td>\n",
       "      <td>2288.7</td>\n",
       "      <td>1070.7</td>\n",
       "      <td>3181.9</td>\n",
       "      <td>211.1</td>\n",
       "      <td>610.2</td>\n",
       "      <td>1.860242</td>\n",
       "      <td>-0.742343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>河北</td>\n",
       "      <td>6076.6</td>\n",
       "      <td>9047</td>\n",
       "      <td>1406.7</td>\n",
       "      <td>2092.6</td>\n",
       "      <td>1161.6</td>\n",
       "      <td>597.1</td>\n",
       "      <td>1968.3</td>\n",
       "      <td>45.9</td>\n",
       "      <td>302.3</td>\n",
       "      <td>-1.501490</td>\n",
       "      <td>-0.812491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>天津</td>\n",
       "      <td>2022.6</td>\n",
       "      <td>22068</td>\n",
       "      <td>822.8</td>\n",
       "      <td>960.0</td>\n",
       "      <td>703.7</td>\n",
       "      <td>361.9</td>\n",
       "      <td>941.4</td>\n",
       "      <td>115.7</td>\n",
       "      <td>171.8</td>\n",
       "      <td>-3.230442</td>\n",
       "      <td>0.752090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>江苏</td>\n",
       "      <td>10636.3</td>\n",
       "      <td>14397</td>\n",
       "      <td>3536.3</td>\n",
       "      <td>3967.2</td>\n",
       "      <td>2320.0</td>\n",
       "      <td>1141.3</td>\n",
       "      <td>3215.8</td>\n",
       "      <td>384.7</td>\n",
       "      <td>643.7</td>\n",
       "      <td>2.150940</td>\n",
       "      <td>-0.451596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>上海</td>\n",
       "      <td>5408.8</td>\n",
       "      <td>40627</td>\n",
       "      <td>2196.2</td>\n",
       "      <td>2755.8</td>\n",
       "      <td>1970.2</td>\n",
       "      <td>779.3</td>\n",
       "      <td>2035.2</td>\n",
       "      <td>320.5</td>\n",
       "      <td>709.0</td>\n",
       "      <td>0.134468</td>\n",
       "      <td>2.634167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>浙江</td>\n",
       "      <td>7670.0</td>\n",
       "      <td>16570</td>\n",
       "      <td>2356.5</td>\n",
       "      <td>3065.0</td>\n",
       "      <td>2296.6</td>\n",
       "      <td>1180.6</td>\n",
       "      <td>2877.5</td>\n",
       "      <td>294.2</td>\n",
       "      <td>566.9</td>\n",
       "      <td>1.039212</td>\n",
       "      <td>-0.040699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>福建</td>\n",
       "      <td>4682.0</td>\n",
       "      <td>13510</td>\n",
       "      <td>1047.1</td>\n",
       "      <td>1859.0</td>\n",
       "      <td>964.5</td>\n",
       "      <td>397.9</td>\n",
       "      <td>1663.3</td>\n",
       "      <td>173.7</td>\n",
       "      <td>272.9</td>\n",
       "      <td>-2.140110</td>\n",
       "      <td>-0.258758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>广东</td>\n",
       "      <td>11769.7</td>\n",
       "      <td>15030</td>\n",
       "      <td>4224.6</td>\n",
       "      <td>4793.6</td>\n",
       "      <td>3022.9</td>\n",
       "      <td>1275.5</td>\n",
       "      <td>5013.6</td>\n",
       "      <td>1843.7</td>\n",
       "      <td>1201.6</td>\n",
       "      <td>5.145964</td>\n",
       "      <td>-0.120924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>广西</td>\n",
       "      <td>2455.4</td>\n",
       "      <td>5062</td>\n",
       "      <td>367.0</td>\n",
       "      <td>995.7</td>\n",
       "      <td>542.2</td>\n",
       "      <td>352.7</td>\n",
       "      <td>1025.5</td>\n",
       "      <td>15.1</td>\n",
       "      <td>186.7</td>\n",
       "      <td>-3.458783</td>\n",
       "      <td>-0.959445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  area      GDP  人均GDP   工业增加值  第三产业增加值  固定资产投资  基本建设投资  社会消费品零售总额  海关出口总额  \\\n",
       "0   山东  10550.0  11643  3502.5   3851.0  2288.7  1070.7     3181.9   211.1   \n",
       "1   河北   6076.6   9047  1406.7   2092.6  1161.6   597.1     1968.3    45.9   \n",
       "2   天津   2022.6  22068   822.8    960.0   703.7   361.9      941.4   115.7   \n",
       "3   江苏  10636.3  14397  3536.3   3967.2  2320.0  1141.3     3215.8   384.7   \n",
       "4   上海   5408.8  40627  2196.2   2755.8  1970.2   779.3     2035.2   320.5   \n",
       "5   浙江   7670.0  16570  2356.5   3065.0  2296.6  1180.6     2877.5   294.2   \n",
       "6   福建   4682.0  13510  1047.1   1859.0   964.5   397.9     1663.3   173.7   \n",
       "7   广东  11769.7  15030  4224.6   4793.6  3022.9  1275.5     5013.6  1843.7   \n",
       "8   广西   2455.4   5062   367.0    995.7   542.2   352.7     1025.5    15.1   \n",
       "\n",
       "   地方财政收入    经济总量水平      人均水平  \n",
       "0   610.2  1.860242 -0.742343  \n",
       "1   302.3 -1.501490 -0.812491  \n",
       "2   171.8 -3.230442  0.752090  \n",
       "3   643.7  2.150940 -0.451596  \n",
       "4   709.0  0.134468  2.634167  \n",
       "5   566.9  1.039212 -0.040699  \n",
       "6   272.9 -2.140110 -0.258758  \n",
       "7  1201.6  5.145964 -0.120924  \n",
       "8   186.7 -3.458783 -0.959445  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data = pca.fit_transform(data) #  fit_transform 表示将生成降维后的数据\n",
    "results = df.join(pd.DataFrame(new_data, \n",
    "                        columns=['经济总量水平', '人均水平'])) # 与原来的数据拼接\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x2957e6bb160>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "# 基础散点图\n",
    "x, y = results['经济总量水平'], results['人均水平']\n",
    "label = results['area']\n",
    "plt.scatter(x, y)\n",
    "plt.xlabel('经济总量水平'); plt.ylabel('人均水平')\n",
    "\n",
    "# 对散点图中的每一个点进行文字标注\n",
    " ## 固定代码，无需深究，拿来即用\n",
    "for a,b,l in zip(x,y,label):\n",
    "    plt.text(a, b+0.1, '%s.' % l, ha='center', va= 'bottom',fontsize=14)\n",
    "\n",
    "# 添加两条竖线\n",
    "plt.vlines(x=results['经济总量水平'].mean(), \n",
    "           ymin=-1.5, ymax=3, colors='red')\n",
    "plt.hlines(y=results['人均水平'].mean(), \n",
    "           xmin=-4, xmax=6, colors='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 广西，河北，福建三地的人均水平和经济总量水平都偏低\n",
    "- 上海的人均经济水平很高，但经济总量水平缺只是略优于均值一点点\n",
    "- 广东的人均经济水平稍次于均值，但经济总量水平很高\n",
    "- ...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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